Using "answers," we selected single-nucleotide polymorphisms (SNPs) on chromosome 22 for comparison of results between single-marker and multi-marker analyses. For the major causal SNP rs2294207 on chromosome 22, both single-marker and multi-marker analyses provided similar results, indicating the importance of this SNP. For the 12 polygenic SNPs on the same chromosome, both single-marker and multi-marker analyses failed to provide statistically significant associations, indicating that their effects were too weak to be detected by either method. The main difference between the two methods was that for the 14 SNPs near the causal SNPs,  Association analysis is often performed using single markers or haplotype analysis of multiple single-nucleotide polymorphisms (SNPs) within adjoining short regions or candidate genes. However, analysis that simultaneously uses multiple markers may be more powerful for detecting several causal genes and, hence, may be more appropriate for complex diseases .n large p). Compared with a regular multiple linear regression (ordinary least squares), LASSO can handle the multicollinearity resulting from the highly correlated markers. Moreover, due to the nature of the L1-penalty, many regression coefficients are exactly zero. Hence, LASSO does both shrinkage and automatic variable selection simultaneously, a form of parsimonious model selection.The least absolute shrinkage and selection operator (LASSO) is a penalized least squares method imposing the L1-penalty on the regression coefficients . BecauseOur main goal in this paper was to explore the performance of LASSO for SNP selection in association analysis. In particular, we compared the relative importance (ranks) of SNPs provided by LASSO to that of SNPs inferred by single-marker analysis.We used the low-density lipoprotein (LDL) phenotype at the first visit for all 200 replicates of the Genetic Analysis Workshop 16 (GAW16) Framingham simulated data sets. This phenotype was adjusted for age, smoking, and diet separately for both sexes and then corrected for medication (HMG-CoA reductase inhibitors) . Becauser2) was computed using R package genetics.For single marker analysis, we used Merlin ,6. The fyi and SNPs xi1,..., xip of ith individual, LASSO minimizes t = 0) to the most complex model including all SNPs as predictors (when t is very large). If a particular SNP becomes a predictor in the ith model, then that SNP tends to stay as a predictor for all bigger models, but this does not always happen. For ranking SNPs, we used this "entry" number that indicates when a particular SNP becomes a predictor in the LASSO solution path. For our analysis, we evaluated the first 10,000 models in the LASSO solution path, using R package lars [For covariate-adjusted phenotype age lars . We usedp-value 4.5 × 10-19 for Replicate 1: for all 200 replicates, this SNP ranked 1.1 on average  had p-values ranging from 3.0 × 10-8 to 3.8 × 10-19 : for all 200 replicates, this SNP ranked 35.8 on average . This may be useful because these neighboring SNPs are not causal. We expected that LASSO would provide better results for the 12 polygenic SNPs. However, this may not have occurred because the strength of their effects was much smaller than the effect of the major causal SNP; thus, for this data set the phenotype appears to be influenced by a single SNP, in which case single-marker analysis will perform better than multi-marker analysis. Hence, our results are inconclusive in terms whether the LASSO analysis provides additional information.k identical predictors, they each get identical coefficients with 1/kth the size that any single one would get if fit alone. On the other hand, LASSO  is somewhat indifferent to very correlated predictors and will tend to pick one and ignore the rest. The elastic net regression  can have the advantages of both ridge and LASSO [The relative advantage of multi-marker analyses over single-marker will depend on the underlying disease model. Other penalized least-squares methods may provide results more similar to single-marker analysis than LASSO. Ridge regression  shrinks the coefficients of correlated predictors toward each other, so they borrow strength from each other. In the extreme case of nd LASSO . We suspGAW16: Genetic Analysis Workshop 16; LASSO: Least absolute shrinkage and selection operator; LD: Linkage disequilibrium; LDL: Low-density lipoprotein; MAF: Minor allele frequency; SNP: Single-nucleotide polymorphismThe authors declare that they have no competing interests.YJS conceived the study, carried out association and LASSO analyses, and drafted the manuscript. TKR carried out all phenotype adjustments. GS developed the concept and set up genotype and map files in the appropriate formats. CCG acquired the data and carried out quality control of the genotypes. DCR participated in the design of the study, helped to draft the manuscript and revised the manuscript. All authors read and approved the final manuscript.


LEPR SNP rs12753193 and body mass index in the prediction of C-reactive protein levels, between the ICAM1 SNP rs1799969 and smoking in the prediction of soluble ICAM-1 levels, and between the PNPLA3 SNP rs738409 and body mass index in the prediction of soluble ICAM-1 levels. These results demonstrate the utility of our approach and provide novel genetic insight into the relationship among obesity, smoking, and inflammation.Testing for genetic effects on mean values of a quantitative trait has been a very successful strategy. However, most studies to date have not explored genetic effects on the variance of quantitative traits as a relevant consequence of genetic variation. In this report, we demonstrate that, under plausible scenarios of genetic interaction, the variance of a quantitative trait is expected to differ among the three possible genotypes of a biallelic SNP. Leveraging this observation with Levene's test of equality of variance, we propose a novel method to prioritize SNPs for subsequent gene–gene and gene–environment testing. This method has the advantageous characteristic that the interacting covariate need not be known or measured for a SNP to be prioritized. Using simulations, we show that this method has increased power over exhaustive search under certain conditions. We further investigate the utility of variance per genotype by examining data from the Women's Genome Health Study. Using this dataset, we identify new interactions between the  LEPR gene in the prediction of C-reactive protein concentration, a marker of inflammation. The second interaction involves the same measure of body fat and a genetic variant of the PNPLA3 gene in the prediction of ICAM-1 levels, also a marker of inflammation. These results are significant because both LEPR and PNPLA3 are linked to the biological response to increased body fat, and inflammation itself is known to be increased in obesity and is thought to contribute to its adverse health effect. Finally, a third interaction was identified between a genetic variant of the ICAM1 gene and smoking in the prediction of ICAM-1 levels. The ICAM1 gene encodes ICAM-1 itself and smoking is known to be an important determinant of ICAM-1 concentrations.Finding gene–gene and gene–environment interactions is a major challenge in genetics. In this report, we propose a novel method to help detect these interactions. This method works by first identifying a subset of genetic variants more likely to be involved in genetic interactions and then testing these variants for interaction effects. Using this method, we were able to identify three previously unknown genetic interactions. The first interaction involves a measure of body fat and a genetic variant of the  Most genetic association studies have so far emphasized the effect of SNPs on mean values of a trait of interest. While this approach has been widely successful in identifying genetic determinants of quantitative traits, it does not make use of differences in the variance of these traits per genotype, which may reflect an alternative mode of genetic influence and signify biological relevance. Several scenarios can result in the variance of a quantitative trait being different when individuals are stratified by SNP genotype (or any other genetic variation) in cross-sectional designs. For examples, such difference could be ascribed to increased variation over time among individuals of a given genotype, increased variation in the homeostatic set-point of a trait (without increased variation over time), or an interaction effect with another SNP or exposure. This latter possibility is particularly attractive since information on variance per genotype could thus be leveraged to reduce the dimensionality of interaction analysis by prioritizing SNPs for further interaction testing.a priori basis will result in a lower number of false positives tests and increased power. Current data reduction methods use evidence of co-adaptation between loci to restrict the search space Several methods have been developed to address the statistical challenge of assessing the degree and nature of interaction between two or more genetic loci In this report, we first derive expressions for the variance according to SNP genotype under models of genetic interactions and demonstrate that the variance of a quantitative trait will differ between the three possible genotypes of a biallelic SNP in the presence of genetic interactions. Building on these derivations, we then simulate data to explore contexts where testing for inequality of variance between genotypes could be used to prioritize SNPs for further interaction testing. Leveraging these observations, we propose a novel method to prioritize SNPs for gene-gene and gene-environment interaction, “variance prioritization”. Variance prioritization entails two steps. In the first step, Levene's test of equality of variance is used to prioritize SNPs for further interaction testing. In the second step, prioritized SNPs are tested for interaction effects against environmental covariates or other SNPs using linear regression. We use data from the Women's Genome Health Study (WGHS) on two inflammation markers, C-reactive protein and soluble ICAM-1, to test whether (1) there is empirical confirmation of SNPs demonstrating inequality of variance between genotypes and (2) whether such SNPs show evidence of either gene-environment or gene-gene interactions.All analyses were performed with approval of the institutional review board of the Brigham and Women's Hospital. All members of the WGHS cohort were participants in the WHS who provided an adequate baseline blood sample for plasma and DNA analysis and who gave consent for blood-based analyses and long-term follow-up.0, β1, β2, and β3 represent constants for the intercept, the marginal effect of G, the marginal effect of C, and the interaction of G and C, respectively. It is assumed the outcome is standardized so it has unit variance conditional on G and C, that is, the error term εi∼N. The variance in Y conditional on genotype of G can be written as:3≠0. Importantly, the variance does not depend on the marginal effect of G on Y (i.e. β1). Furthermore, as we did not specify the distribution form of the interacting covariate C, Equation 3 can be generalized to different types of interaction such as dichotomous environmental exposures, continuous environmental exposures or the three genotype classes of a biallelic SNP. The total variance of Y is given by:2) can be defined as:3) can be defined as:Consider a biallelic SNP G with minor allele frequency p and in Hardy-Weinberg equilibrium. Without loss of generality and assuming an additive genetic effect of G on a quantitative trait of interest, G can be encoded as -2p, 1-2p, or 2-2p such that mean genotype equals zero. Assume that there is an additive interaction between G and a covariate C  such that the quantitative trait Y for the ith individual is represented as:k samples have equal variances and is less sensitive to deviation from normality than the alternative Bartlett test Levene's test 0: Ha: i,j).HY with sample of size N divided into k subgroups, where iN is the sample size of the ith subgroup, the Levene test statistic is defined as:jth observation of the ith subgroup, ith subgroup, Given a variable k−1 and N−k degrees of freedom at a significance level of The Levene test rejects the hypothesis that the variances are equal if:1) interacting with a covariate (denoted C1) in determining the value of a quantitative trait  nor the power to detect the interaction. For simplicity, only results from the additive genetic model  are shown but similar conclusions can be derived from more general models of association. Under a range of models for the main effect of C1 (on the quantitative trait), allelic frequency of SNP1, and interaction between SNP1 and C1, we tested the power of Levene's test to reject the null hypothesis that the variance of Y does not vary between subsets of individuals with differing genotypes (of SNP1). In all simulations, the quantitative trait was assumed to have a normally distributed error term with mean equal to zero and variance equal to 1. The covariate was normally distributed with mean equal to zero and variance equal to 1. Interactions are expected to be modest at best and simulations were therefore restricted to interaction effects explaining up to 1% of the quantitative trait's total variance. Each condition was simulated 1,000 times using 15,000 individuals unless otherwise specified. In a second set of simulations, we tested the power of Levene's test of equality of variance to detect SNP-SNP interactions. These simulations are based on a model similar to the one previously introduced, with the exception that the normally distributed covariate (C1) is replaced by a second, independent, SNP (denoted SNP2). To simplify simulations, both SNPs were assigned the same allelic frequency as well as the same main effect (i.e. SNP-Y association). Varying the strength of the SNP1- SNP2 interaction, we simulated each condition 1,000 times using 15,000 individuals unless otherwise specified.We used simulations to estimate the power of Levene's test to detect differences in variance of a quantitative trait between individuals of differing genotypes. We first simulated the case of a SNP (denoted SNPLeveraging two observations that (1) the signature of a SNP-covariate interaction may be inequality of variance between SNP genotypes and (2) a test for inequality of variance is independent of a subsequent direct test of interaction, we propose a novel method to prioritize SNPs for gene-gene and gene-environment interaction, termed “variance prioritization”. This prioritization procedure first applies Levene's test of variance equality to selects SNPs below a pre-specified threshold and then tests these “prioritized” SNPs for gene-environment or gene-gene interaction using standard linear regression models see , with BoIndependence of variance per genotype from estimates of interaction effects under the null of no interaction is important because the validity of the method rests on having correct type I error. A proof of independence between variance per genotype and estimation of interaction effect under the null of no interaction is presented in The study population was derived from the Women's Genome Health Study (WGHS) All baseline blood samples underwent measurement for high-sensitivity CRP (hsCRP) via a validated immunoturbidometric method . CRP is an inflammatory biomarker with strong prognostic value for development of metabolic syndrome, diabetes, myocardial infarction, and stroke. Log-transformed CRP levels were used in all analyses. Soluble ICAM-1 is an inflammatory marker that has been associated with several common diseases such as diabetes, heart disease, stroke and malaria. Circulating plasma sICAM-1 concentrations were determined using a commercial ELISA assay  The environmental factors tested for interaction included self-reported body mass index, menopause status, hormone replacement therapy use, smoking, age and exercise, expressed as quintiles of total energy expenditure spent from all recreational activities.DNA samples were genotyped with the Infinium II technology from Illumina. Either the HumanHap300 Duo-Plus chip or the combination of the HumanHap300 Duo and I-Select chips were used. In either case, the custom content was identical and consisted of candidate SNPs chosen without regard to allele frequency to increase coverage of genetic variation with impact on biological function including metabolism, inflammation or cardiovascular diseases. Genotyping at 318,237 HumanHap300 Duo SNPs and 45,571 custom content SNPs was attempted, for a total of 363,808 SNPs. Genetic context for all annotations are derived from human genome build 36.1 and dbSNP build 126.−6. SNPs with minor allele frequency >1% in Caucasians were used for analysis. 339,596 SNPs were left after quality control. All statistical analyses were done with R, unless specified otherwise. All analyses were performed on individuals of self-reported Caucasian ancestry, with confirmation using a principal component analysis. A total of 21,799 individuals were included in our final dataset.SNPs with call rates <90% were excluded from further analysis. Likewise, all samples with percentage of missing genotypes higher than 2% were removed. Among retained samples, SNPs were further evaluated for deviation from Hardy-Weinberg equilibrium using an exact method and were excluded when the P-value was lower than 101) interacting with a single covariate, C1 ). We then compared the results of variance prioritization with a “conventional” method correcting for 340,000 tests. Each condition was simulated 2,000 times. As illustrated in Nevertheless, as can be observed in −13. 3 such that 0.2% or 0.25% of the variance was explained by the interaction. In these simulations, all SNPs with Levene's test P-value lower than the specified threshold were prioritized and tested for interaction against all other SNPs, prioritized or not (with appropriate correction for multiple hypothesis testing). This approach was shown (by simulations) to be more powerful than testing for pairwise interactions between Levene's test prioritized SNPs only, which was underpowered as compared to an exhaustive search (data not shown). As illustrated in Similar simulations were performed for SNP-SNP interactions see . For sim−7 for CRP . Because the LEPR SNPs are in linkage disequilibrium, only rs12753193 will be further considered since it had the most significant Levene's test P-value. Two SNPs, the PNPLA3 SNP rs738409 and the ICAM1 SNP rs1799969, had genome-wide significant Levene's test P-value for sICAM-1 (see LEPR SNP rs12753193 interacted with BMI in the prediction of CRP levels (P = 7.2×10−10), the ICAM1 SNP rs1799969 interacted with smoking status in the prediction of sICAM1 levels (P = 4.8×10−9) and the PNPLA3 SNP rs738409 interacted with BMI in the prediction of sICAM1 levels (P = 1.6×10−7).To empirically test whether some SNPs exhibit inequality of variance between genotypes and whether these effects might be due to interactions, we applied Levene's test of equality of variance to ongoing genome scans of two inflammatory markers, C-reactive protein (CRP) and soluble ICAM-1 (sICAM-1). Only SNPs with genome-wide significant Levene's P-value were selected for subsequent study in order to ensure they represent true positives. From the analysis of our sample of 21,799 women, 23 SNPs had a Levene's test P-value lower than 1.5×10AM-1 see . To furtAM-1 see . The LEP−29 for the LEPR SNP rs12753193 in the CRP analysis, the P-value was 9.1×10−21 after adjustment for the interaction for BMI. Similarly, Levene's test P-values were 2.1×10−9 for the ICAM1 SNP rs1799969 and 1.9×10−10 for the PNPLA3 SNP rs738409 in the sICAM-1 analysis but were slightly less significant after adjustment for the intraction with BMI and smoking status, respectively, at 3.3×10−9 and 1.6×10−7. Overall, these interactions explained 0.18% of CRP total variance, and 0.16% and 0.13% of sICAM-1 total variance for the ICAM1 and PNPLA3 interactions, respectively. Neither the LEPR SNP rs12753193 nor the PNPLA3 SNP rs738409 was associated with BMI (P>0.05). The ICAM1 SNP rs1799969 was not associated with smoking status (P>0.05). These data collectively suggest that individual interactions are likely to have modest effects, and that additional covariates and/or SNPs might interact with the validated SNP.We sought to determine the contribution of the three interactions to Levene's test of inequality of variance. While Levene's test P-value was 1.6×10Genetic effects on the variance of quantitative traits have been largely ignored so far in the genetic analysis. As shown in this report, differences in the variance per genotype can reflect interaction effects and these differences can be leveraged to prioritize SNPs for interaction testing. The proposed method, variance prioritization, presents the distinct advantage that it does not rely on the marginal effect of SNPs on the means to prioritize them. Importantly, it can be used in combination with prioritization based on marginal effects (on the means) if it is assumed all interacting SNPs have a main effect on the trait of interest. In such case, combining variance prioritization to prioritization based on a pre-defined marginal effect P-value provides the greatest power as compared to each procedure individually, if the assumption that interacting SNPs have main effects is true (data not shown). Another interesting property of this method is the fact that the interacting covariate need not be measured or even known to arouse suspicion of an interaction effect, perhaps leading to novel biological insight.The goal of any genetic prioritization method is to form a subset of genetic variants enriched for the effect of interest. Variance prioritization results in substantial reduction in multiple hypothesis correction and hence increases power by using the information embedded within the variance per genotype, which we showed is independent from subsequent interaction testing. In this case, a frequentist approach was adopted since prior knowledge on the nature of the contribution of genetic interactions to complex traits is currently scarce. Our simulations have shown that the optimal Levene's test P-value threshold for variance prioritization will depend upon the allelic frequency, sample size, association of the covariate with the quantitative trait, and expected strength of the interaction. In most cases, all of these parameters can be directly observed with the exception of the interaction strength. The optimal threshold can therefore be empirically selected through simulations when the potentially interacting covariate (or SNP) is characterized and the expected interaction effect size defined.Inequality of variance between genotypes could be the result of factors other than interactions, such as increased variability. Furthermore, the heteroscedacity of variances also depends on an assumption of homogeneity of variances within genotype, which could be violated if variances tend to be proportional to means. This might reflect an inappropriate choice of scale for the phenotype and a variance-stabilizing transform like the log or square root might then be indicated. Linkage disequilibrium with variants of large effect sizes could also theoretically lead to inequality of variance between genotypes. Conversely, non-additive interactions or SNPs with a multiplicity of interactions may not result in deviation of Levene's test. Failure to prioritize “interacting” SNPs or prioritization of “non-interacting” SNPs can theoretically lead to decreased statistical power but should not lead to inflated type I error since the first step is a screening test followed by confirmatory analysis. While care was taken to simulate models with plausible effect sizes , the scenarios described in this report undoubtedly represent simplified situations. Nevertheless, these simulations suggest that variance prioritization has increased power to detect genetic interactions of very modest strength in situations where conventional methods perform poorly.LEPR is also expressed by the liver, the source of circulating plasma CRP, and physiological concentrations of leptin stimulate expression of CRP in primary hepatocytes LEPR SNP rs12753193 genotypes was due to the interaction with BMI: the statistical interaction may only capture a small fraction of the underlying biological interaction between leptin levels and its receptor.Genetic effects on variance in an empirical dataset led to the identification of three novel interactions. Given the strength of the observed interactions, they would have also been identified with traditional methods. The significance of these results resides in (1) the existence of SNPs with inequality of variance between genotypes at genome-wide significance and (2) the presence of biologically plausible interactions between these SNPs and environmental factors. The first interaction involved the leptin receptor SNP rs12753193 in the prediction of CRP. Leptin is a protein synthesized and secreted by the adipose tissue whose main function is to regulate appetite trough interaction with the leptin receptor in the hypothalamus LEPR SNP rs12753193 is known to be associated with CRP concentration at a genome-wide level The ICAM1 SNP rs1799969 in the prediction of sICAM-1 levels. The latter SNP is a coding non-synonymous SNP (G241R) known to be associated with sICAM-1 levels ICAM1 non-synonymous SNP  and smoking was recently described with regards to susceptibility to coronary artery disease The second interaction observed involved the PNPLA3 SNP rs738409 in the prediction of sICAM-1. PNPLA3 encodes a protein of unknown function that belongs to the patatin-like phospholipase family. The methionine allele of the missense PNPLA3 SNP rs738409 (Ile148Met) has recently been associated with increased hepatic fat levels, hepatic inflammation and plasma levels of liver enzymes (traits often linked to insulin resistance and obesity) −7; Beta = 4.9 ng/mL per minor allele) while being associated with a greater effect of BMI on sICAM-1 levels.The third interaction identified involved the LEPR SNP rs12753193, the ICAM1 SNP rs1799969 and the PNPLA3 SNP rs738409 would still have been selected as candidates for interaction effects, perhaps leading to novel biological hypotheses.In this report, we demonstrated that the presence of gene-gene or gene-environment interactions can result in changes in variance of a quantitative trait per genotype and that these changes can be exploited to prioritize SNPs for interaction testing. We used this property of quantitative trait interactions on a real dataset and discovered three new, biologically relevant, interactions. Variance prioritization presents two advantages over other methods. First, it does not depend on the marginal effect of SNPs on the means. Second, it can identify SNPs likely to be involved in an interaction even when the interacting genetic or environmental covariate(s) is not measured. For instance, had BMI or smoking status not been assessed in the WGHS, the Figure S1(A) QQ-plot of interaction P values in the presence of inequality of variance between genotypes (95% CI). (B) QQ-plot of interaction P values when selecting SNPs with Leneve's P<0.05 (95% CI).(0.61 MB TIF)Click here for additional data file.Table S1−7 from the analysis of C-reactive protein and soluble ICAM-1 in 21,799 women from the WGHS.SNPs with Levene's test of inequality of variance P-value lower than 1.5×10(0.07 MB DOC)Click here for additional data file.Text S1Proof of independence between variance per genotype and estimation of interaction effect under the null of no interaction.(0.07 MB DOC)Click here for additional data file.


CFH, C2/BF, LOC387715/HTRA1) already known to contain important genetic risks for AMD. One additional locus (C3) reported during the course of this study was identified and replicated in an additional study group. Tag-SNPs and haplotypes for each locus were evaluated for association with AMD in additional cohorts to account for population differences between discovery and replication subjects, but no additional clearly significant associations were identified. Relying on a significant genotype tests using a log-additive model would have excluded 57% of the non-replicated and none of the replicated loci, while use of other SNP features and clustering might have missed true associations.Genome-wide association studies (GWASs) assess correlation between traits and DNA sequence variation using large numbers of genetic variants such as single nucleotide polymorphisms (SNPs) distributed across the genome. A GWAS produces many trait-SNP associations with low p-values, but few are replicated in subsequent studies. We sought to determine if characteristics of the genomic loci associated with a trait could be used to identify initial associations with a higher chance of replication in a second cohort. Data from the age-related eye disease study (AREDS) of 100,000 SNPs on 395 subjects with and 198 without age-related macular degeneration (AMD) were employed. Loci highly associated with AMD were characterized based on the distribution of genotypes, level of significance, and clustering of adjacent SNPs also associated with AMD suggesting linkage disequilibrium or multiple effects. Forty nine loci were highly associated with AMD, including 3 loci ( Genetic variation altering the risk of developing common and complex traits or diseases is being discovered using genome wide association studies (GWASs) Although a GWAS is a powerful means for discovering trait-associated variants A number of strategies have been proposed to identify the SNPs more likely to be truly associated with the trait Generally SNPs without common minor alleles and accurate genotype call rates are excluded from subsequent analysis, because SNPs without these features are more likely to represent artifacts 2 tests and would be more likely to have nearby SNPs also associated with disease (due to linkage disequilibrium). The log-additive model is less sensitive to changes driven only by differences in heterozygote frequencies between cases and controls and thus is impacted less by this common effect of genotyping error or population stratification http://view.ncbi.nlm.nih.gov/dbgap-controlled), accession number phs000001.v1.p1.) on subjects with age-related macular degeneration .In this study, we sought first to test the idea that features of individual SNPs associated with disease and clustering of nearby significant SNPs  would be useful in selecting loci from genetic association studies for replication CFH; Gene ID 3075) C2/BF; Gene IDs 712/629) locus C3; Gene ID 718) locus were published LOC387715 (Gene ID 387715) and the beginning of the HTRA1 (Gene ID 5654) gene was the second major locus identified AMD is a common trait with well established genetic risks. By the year 2020, three million people in the United States are expected to have advanced AMD that often leads to severe vision loss The age-related eye disease study (AREDS) was a multi-centered clinical trial, which demonstrated the protective effect of antioxidants and zinc on preventing the exudative complications of AMD −4), one additional locus (C3) reported during the course of this study Herein, we report that the AREDS GWAS identified the loci already known at the time the data was deposited into dbGaP, namely the CFH, LOC387715/HTRA1, and C2/BF loci. Of the 57 other loci associated with AMD . These subjects were recorded as non-Hispanic white (97.6%), non-Hispanic black (2%), Hispanic (0%), Asian or pacific islander (0%), or other (0.34%). Access to the raw data from the AREDS trial was provided via the National Eye Institute. Replication of loci identified in the AREDS GWAS on the Mayo subjects used 744 individuals . The use of these subjects was approved by the institutional review board of the Mayo Clinic, written consent was obtained from all subjects and the study was performed in accordance with the Helsinki declaration. Diagnosis was determined by review of fundus photographs as previously described −4 might represent a significant association with disease and this threshold for replication was employed. Individual genotype counts were not available until late in this study due to the time required to gain access to the dataset and thus were not available initially to perform statistical analyses. Therefore, we reviewed the allele association data for all SNPs with p-values less than 0.0001. The allele frequencies of the 100,000 SNPs were reviewed along with the P-values for association with AMD, Hardy-Weinberg equilibrium (HWE), and support from adjacent SNPs. An attempt was made to replicate all SNPs associated with AMD, except those in the artifact groups . Only those SNPs deemed candidate tag-SNPs by ldSelect with an Illumina score greater than or equal to 0.60 and a MAF of greater than or equal to 0.05 were considered for further selection using this algorithm. Tag-SNPs were ultimately selected based upon a functional ranking system wherein non-synonymous coding SNPs were preferentially selected among the tag-SNP candidates in each LD bin, followed by synonymous coding SNPs, SNPs from 5′ untranslated regions (UTRs), SNPs from 3′ UTRs, SNPs from 5′ flanking UTRs, SNPs from 3′ flanking UTRs, and finally SNPs from intronic regions. If an LD bin contained more than one tag-SNP with the same highest function ranking, the SNP with the highest MAF was selected as the tag for that bin. This algorithm was applied to tag-SNPs from the confirmed, confirmed rare, valid, and valid rare categories  were genotyped. Tag-SNPs were selected by inspecting the genomic region of each replicated locus for genes within 20 kb or within an LD block of any SNP associated with AMD. Caucasian data from HapMap in the genomic region of each gene and 2 kb upstream and downstream were used for selecting tag-SNPs. Illumina genotyping scores were obtained for SNPs within the selected genomic regions. SNPs with a score greater than 0.6 were used for further analyses. After merging the files containing Illumina scores with the list of candidate tag-SNPs generated using ldSelect tegories . All othWe designed an Illumina GoldenGate™ assay for these 243 SNPs such that ninety three percent of the SNPs had Illumina SNP scores >0.6. Genomic DNA samples (250 ng) were genotyped following the Illumina protocol . Genotype calls were made using the Genotyping module of the BeadStudio 3 software. Genotype clusters were reviewed using the replicate and heritability information of 16 control CEPH trios to refine clustering. Initial laboratory quality assurance relied on the GenCall score, a quality metric indicating the reliability of called genotypes that is generated by the BeadStudio software. The GenCall_10 refers to the 10th percentile GenCall score in a particular distribution of GenCall scores. For loci, it represents the 10th percentile rank for all GenCall scores for that locus. Samples with GenCall_10 scores below 0.4 and/or call rates below 90% and SNPs with call rates below 90% were failed. Quality control for genotype call was assessed by concordance for the control CEPH trio DNA replicates and the sample replicates within each plate (2 per 96 well plate).Upon receipt of genotype intensities, all SNPs were validated by reviewing the accuracy of genotype discrimination (clustering) methods. SNPs or subjects with call rates lower than 95% or not in Hardy-Weinberg equilibrium (P<0.001) were excluded from further analyses. Any SNP presented in tables in the main manuscript have HWE p-values of 0.05 or higher in controls, unless pointed out in the text. Single SNP analyses on genotype distributions 2) were publically available on dbGaP at the time this study was initiated and these statistical tests were used to select SNPs for replication. −4 that were identified. Inspection of the loci suggested that they could be characterized based on clustering (support from adjacent SNPs for association with AMD), Hardy-Weinberg equilibrium (HWE), and minor allele frequency. Forty nine loci were highly associated with AMD and had features suggestive of valid genotyping results . One AREDS SNP  could not be genotyped on the Illumina platform, leaving 57 AREDS SNPs that were studied. Three tag-SNPs  were studied in place of rs3895626. The details of these 57 AREDS SNPs are presented in LOC387715/HTRA1, and C2/BF) at the time of the release of the AREDS GWAS were associated with AMD. Loci from the X chromosome were excluded, because the hemizygous males were not analyzed separately from females by dbGaP and the association analysis could not be interpreted from the dbGaP data at the start of the study.Allelic p-values (2×2 χC3) of the 57 AREDS SNPs was clearly replicated in the Mayo subjects . We have found the log-additive model for genotype distribution a useful parameter in identifying SNPs that are not truly associated with disease in recent projects 2 analysis). Other features such as level of significance  and minor allele frequency were not useful discriminators in this dataset  and replicated loci (1) was not large enough to build a predictive model, we inspected the features of both groups of AREDS SNPs (replicated and non-replicated) for insights that might facilitate efficient selection of SNPs for replication . Cluster dataset .−4). Three of these 49 loci were already known to be associated with AMD and the SNPs meeting our replication criteria  has been replicated recently in other studies, while this project was ongoing We identified 49 loci in the AREDS 100,000 SNP GWAS that were associated with AMD  polymorphism corresponds to the electrophoretically slow (Arg) and fast (Gly) forms of C3 and may be the causative polymorphism due to the fast forms probable involvement in other diseases including renal transplant survival, Chagas disease cardiomyopathy, and type II mesangiocapillary glomerulonephritis C3 is thus the third complement locus strongly implicated in the risk of AMD, in addition to CFH and C2/BF.The observation that CFH and LOC387715/HTRA1 loci which had the two highest p-values in the study, 1.4×10−11 and 2.5×10−7 respectively. However, the p-values for the C2/BF and C3 loci were similar to p-values for other loci which failed to replicate  p-values were available to us at the start of this project and we based our selection of loci for replication on this metric, we can make post-hoc comparisons to the genotype distributions that later became available. Relying on the log-additive model would have been superior to χ2 tests based on allelic (2×2) or genotype (2×3) tests for association. t test, P = 2.22×10−13). For example, reliance on the log-additive genetic model test rather than the 2×2 allelic χ2 would have excluded 32/56 of the non-replicated SNPs and none (0/4) of the replicated SNPs/loci at the same level of significance (P<10−4).Another commonly used strategy is to apply a log-additive genetic model (trend test) to the genotype distributions for ranking association with disease As noted in the introduction, clustering of SNPs significantly associated with case-control status is thought to provide support for the association. While this study was ongoing, we observed clustering of SNPs associated with AMD in toll-like receptor (TLR) loci The AREDS subjects came from 11 different clinics representing different regions of the USA Our experience with the AREDS 100,000 SNP GWAS and other studies suggests some simple guidelines for designing GWASs and replication studies that would improve the ability to efficiently detect disease associated loci Table S1Features of the 56 of 57 AREDS SNPs highly associated with AMD (P<10E-04) in the AREDS 100,000 SNP genome wide association study that were not replicated are shown in the top section of this table. SNPs from the CFH, BF/C2 and LOC387715/HTRA1 loci already known to be associated with AMD and the C3 locus replicated in this study are shown in the bottom section of this table. Abbreviations: HWE, Hardy-Weinberg equilibrium; SNP, single nucleotide polymorphism; AREDS, age-related eye disease study.(0.17 MB DOC)Click here for additional data file.Table S2Biological information and statistical tests of allele and genotype association with AMD in the AREDS subjects (dbGAP data) and Mayo subjects. The replicated SNP in C3 is highlighted.(0.17 MB DOC)Click here for additional data file.Table S3Genotype distribution for all 300 SNPs genotyped on Mayo samples, which includes 57 AREDS SNPs, 225 tagSNPs, and 18 non-synonymous SNPs.(1.85 MB DOC)Click here for additional data file.Table S4Genotype distribution results for 6 SNPs with significant log-additive p-values in Mayo samples compared to genotype distribution results for the same SNPs genotyped in AREDS samples (replication cohort).(0.06 MB DOC)Click here for additional data file.


The genome sequence and a high-density SNP map are now available for the chicken and can be used to identify genetic markers for use in marker-assisted selection (MAS). Effective MAS requires high linkage disequilibrium (LD) between markers and quantitative trait loci (QTL), and sustained marker-QTL LD over generations. This study used data from a 3,000 SNP panel to assess the level and consistency of LD between single nucleotide polymorphisms (SNPs) over consecutive years in two egg-layer chicken lines, and analyzed one line by two methods  to identify markers associated with egg-quality and egg-production phenotypes.2 > 0.2 at < 2 Mb) and remained high after one generation (correlations of 0.80 to 0.92 at < 5 Mb) in both lines. Single- and 3-SNP regression analyses using a mixed model with SNP as fixed effect resulted in 159 and 76 significant tests (P < 0.01), respectively, across 12 traits. A Bayesian analysis called BayesB, that fits all SNPs simultaneously as random effects and uses model averaging procedures, identified 33 SNPs that were included in the model >20% of the time (φ > 0.2) and an additional ten 3-SNP windows that had a sum of φ greater than 0.35. Generally, SNPs included in the Bayesian model also had a small P-value in the 1-SNP analyses.The LD between markers pairs was high at short distances (rHigh LD correlations between markers at short distances across two generations indicate that such markers will retain high LD with linked QTL and be effective for MAS. The different association analysis methods used provided consistent results. Multiple single SNPs and 3-SNP windows were significantly associated with egg-related traits, providing genomic positions of QTL that can be useful for both MAS and to identify causal mutations. Genetic variation within breeds or lines is the primary source for genetic improvement in livestock. Large rates of genetic improvement have been achieved in poultry by selecting breeders with high breeding values based on phenotypic information. Following advances in molecular genetic technologies and availability of DNA markers, interest has rapidly grown in identifying quantitative trait loci (QTL) that genetically control important traits for application in marker-assisted selection (MAS) programs ,2. VarioUnderstanding the linkage disequilibrium (LD) that exists in a population is necessary for effective application of MAS. Linkage disequilibrium measures the non-random association of alleles at two or more loci in the population. Within a closed breeding population, the extent of LD is affected by the distance between the loci and historical effective population size . In geneA variety of statistical methods can be used for analysis of high-density SNP data to identify QTL regions or SNPs associated with phenotypic traits using LD in closed breeding populations. These methods were reviewed by Dekkers et al.  and someData from two elite breeding lines of egg-laying chickens from Hy-Line Int., Line 1 (White Egg) and Line 2 (Brown Egg), were analyzed. The data included average adjusted progeny performance of sires for two egg-production and 10 egg-quality traits, pedigree information, and genotypes for a 3,000 SNP panel. Phenotypic data for each sire were the average performance of his daughters, with each daughter's phenotype adjusted for random effects of half of the Estimated Breeding Value (EBV) of the daughter's dam and for the fixed effect of hatch, as estimated by routine genetic evaluation procedures utilized by Hy-Line, International. Traits were classified into two categories based on age of measurement: early  and late traits (measured at week 38). Early traits included Early Albumen Height , First 3 Egg Weight , Early Egg Weight , Early Production , Early Shell Quality , Sexual Maturity  and Early Yolk Weight . Late traits included Late Albumen Height , Late Egg Weight , Late Production , Late Shell Quality  and Late Yolk Weight .The DNA samples from sires in two consecutive generations (2003 and 2004) and from their paternal ancestors (3 to 6 generations back) were genotyped for about 3000 SNPs using an Illumina SNP array  that hasLinkage disequilibrium was analyzed using the SNP data for chromosomes 1 and 4 on roosters from the two lines from two consecutive years (2003 and 2004), reflecting two subsequent generations. These two chromosomes were chosen because they had sufficiently larger numbers of SNPs to obtain accurate results. In addition, results are expected to be representative of other chromosomes (see discussion section). The numbers of genotyped roosters were 60 and 67 for Line 1 and 2, respectively, in year 2003; and 42 and 20 in year 2004. Chromosomes 1 and 4 had 449 and 184 genotyped SNPs, which were spaced on average 450–500 kb apart. Because low minor allele frequency (MAF) can skew measures of LD , only SN2 and r. To assess the decline of LD with distance, r2 was plotted against distance and a moving average of LD was calculated. A predicted LD curve was estimated by fitting the Sved [2 data: r2 = 1/(1+4*Ne *d), where d is distance in Morgans estimated using 2.4 cM/Mb for both chromosomes, and Ne is the effective population size. This model was fitted to data from each line and each chromosome using methods described by Zhao et al. [The SNP data were analyzed using Haploview  and Powethe Sved  equationo et al. . To asseFor the purpose of detecting SNPs associated with traits, which was performed in line 1 only, Illumina's top A and B allele calls  for SNPsInitial analyses of SNP data of Line 1 showed 22 roosters with genotypes for one or more SNPs that were incompatible with the genotype of the sire . For most of these individuals, incompatibility was limited to one SNP, which was considered as a genotyping error  and coded as missing. For individuals with multiple incompatible SNPs (n = 21 to 60), parentage testing was carried out to determine the most likely sire. Briefly, SNP genotypes of these individuals were compared with the SNP genotypes of all genotyped individuals and the number of incompatible SNP genotypes was counted for each pair of individuals. For each of these individuals, there was a single sire in the previous generation with fully compatible SNP genotypes and these were, therefore, used as their sire in the analysis.Association analyses were conducted using two types of analyses: 1) SNP-wise analyses, in which each SNP or each window of three consecutive SNPs was fitted separately, along with a polygenic effect, and 2) Genome-wise analyses, in which all SNPs were fitted simultaneously, using the BayesB method of Meuwissen et al. .Two different mixed models were used for the SNP-wise analyses: a 1-SNP model and a 3-SNP model. Both models were implemented using the Mixed Model procedure of SAS based on programs developed by Hassen et al.  and usinY is an n × 1 vector of average adjusted daughters' performance for n siresXgen is design matrix which relates observations to fixed generation effectsGen is a vector of solutions for the generation effectXi is the design matrix for SNP effects, with elements equal to the number of 0 alleles carried by each sire. For the 1-SNP analysis, this is an n × 1 matrix with elements equal to the number of 0 alleles carried for SNP i. For the 3-SNP analysis, this is an n × 3 matrix with elements corresponding to the number of 0 alleles for SNPs i-1, i, and i+1.βi is a 1 × 1  or a 3 × 1  vector of SNP allele substitution effects, fitted as fixed effects.Z is an incidence matrix relating random sire effects to the vector of observationss is a vector of random effects of sires, assumed distributed normal with mean 0 and variance AA is the additive genetic relationship matrix [p matrix  derived e is a vector of random error effects, which was assumed distributed normal with mean 0 and variance DD is a diagonal matrix with elements equal to the reciprocal of the number of progeny included into each sire's progeny average, and Estimates of y ASReml  using thP-values were obtained from a chi-square distribution with one (for the 1-SNP model) or three (for the 3-SNP model) degrees of freedom.The significance of SNP effects was obtained from a likelihood ratio test of the full model that included SNP effects to a reduced model without SNP effects . AssociaThe BayesB method of Meuwissen et al.  was usedY, Xi, βi, Z, s, and e are as in the 1-SNP analysis model described previously, μ is an intercept, Ii is a 0/1 indicator variable indicating whether SNP i is included in the model (see below), and the summation is over all SNPs that are segregating at MAF ≥ 0.05. Substitution effects βi were fitted as random with variance i = 1), 1-π, was set equal to 0.05 [X-2, with parameters v = 4.2 [k is the expected number of segregating QTL, which was set equal to 200 and S parameter used here differs from what was used by Meuwissen et al. [k, as in Meuwissen et al. [μ, s, βi, and running 100 cycles of the Metropolis-Hastings algorithm within each Gibbs chain for sampling i = 0).where  to 0.05 . The pri v = 4.2  and , whn et al.  because en to be ,25 rathen et al. . The Mars, and polygenic variance, s was assumed Normal , with v = 4.2 and Polygenic effects, Sorensen  in the ci = 1 for each SNP gives a marginal posterior probability (φi) of a SNP being associated with the trait, conditional on all other markers included in the model. An association was considered significant if φi > 0.2. Given a prior probability of a SNP having non-zero variance of 1-π = 0.05, φi > 0.2 can be interpreted as the probability that a SNP has non-zero variance at least 4 times more often than expected. However, this number is likely underestimated as effects of a QTL could be distributed across multiple correlated SNPs that may be genotyped in the QTL region (see discussion). Furthermore, if the sum of φi for a window of 3 adjacent SNPs was greater than 0.35 , that 3-SNP window was considered to be significantly associated with the trait. The rationale for applying this approach is that the sum of φi for 3 adjacent SNPs might represent an important signal from a region in which no single SNP was above the φi > 0.2 threshold.The fraction of cycles with IIn addition to estimating trait polygenic and residual variances by ASReml, as described previously, they were also estimated using the Bayesian model without fitting SNP effects. In these analyses, the mean of the prior distribution of the polygenic variance was set equal to the estimate obtained from ASReml.Plots of the estimated LD and distance between marker pairs displayed a sharp decline of LD with increasing distance Figure . Lines s2, and this pattern was consistent across both years and chromosomes.The LD between pairs of markers that were segregating in both years  was relatively consistent across years, as quantified by the correlation between LD , indicating that there was little to no consistency of LD between the lines  in the 1-SNP analyses. For the genome-wise BayesB analyses, 33 SNPs were included in the model more than 20 percent of the time (φi >0.2) across all traits. The SNPs with φi > 0.2 are tabulated in Additional file P-value in the 1-SNP analysis  analysis was 0.71 for all tested SNPs and 0.83 for SNPs with φi > 0.2. The same pattern of agreement between the analyses also occurred for estimates of SNP effects from the genome-wise and the SNP-wise analyses  >0 and ≥ 0.05, respectively. The SNP-wise 1- and 3-SNP analyses (2 Mb average 3-SNP window size) resulted in 159 and 76 tests with φi for a window of 3 adjacent SNPs , identified 10 additional regions with a sum of φi greater than 0.35. "Additional file φi > 0.35, which included two windows for EYW on GGA1 and 25; two overlapping windows for EEW on GGA1; 3 overlapping windows for EPS on GGA7; one window for LAH on GGA19 and 2 overlapping windows for SM on GGA20. Four out of 10 windows with a sum of φi > 0.35 had P <0.01 based on the SNP-wise 3-SNP analyses.Summing 2>0.2 at <2 Mb) but declined rapidly with distance. This high LD was expected for commercial layer lines, which exhibit lower heterozygosity than broiler lines. The decline of LD with distance behaved as expected, with the moving average following the predicted LD curve based on the estimate of the historical effective population size. This LD pattern was consistent between lines, years, and the two chromosomes analyzed. A previous report on broiler lines [The LD in the two lines evaluated was high at short distances  that were estimated in these two layer lines were surprisingly small but it should be noted that the standard errors associated with those estimates were large. Estimates of Ne were larger for Line 2 than Line 1 in the current study, which reflects less inbreeding among brown, than white, egg layers ,29. Esti2. The within-line LD correlations, however, rapidly declined with increasing distance between markers. Heifetz et al. [There was a strong correlation within each line of LD between consecutive years. The LD remained high at short distances after one generation (correlations of 0.80 to 0.92 at < 5 Mb), indicating that markers in high LD with a QTL at short distances will retain high LD in progeny, which is essential for MAS. High correlations were observed both for LD measured by r and rz et al.  also shoIn the current study, 34% of successfully genotyped SNPs were segregating (MAF>0) in Line 1. This level of heterozygosity is consistent with the report by Muir et al.  of aboutThe overall findings indicate that LD at short distance was sufficient to detect QTL. Markers in high LD with a QTL at short distances are expected to retain high LD in the following generation and will consequently be effective for MAS.The results of the current study showed general agreement in identification of associations of markers with traits between use of SNP-wise and genome-wise association analyses. Estimates of SNP effects from the genome-wise analyses were, however, considerably smaller than those obtained from the 1-SNP analyses, primarily because effects were fitted as random and, therefore, 'shrunken'. Estimates of the effect of a particular SNP and its standard deviations  in the genome-wise model with and without SNP effects also resulted in a 38% decrease in the estimate of polygenic variance by fitting SNP effects . These results indicate that markers explained an important part of polygenic additive variance, however, about half of the genetic variance was not explained by the markers, which suggests the need for developing higher density SNP panels for genotyping.Application of one of the first widely available SNP panels for chickens allowed genotyping at a higher marker density than most previous studies and, therefore, revealed many previously undetected QTL regions. Two adjacent SNPs on GGA18 and one SNP in each of GGA5 and GGAZ were significantly associated with EAH. Considering the short distance (~110 kb) between the two adjacent SNPs on GGA18, they are likely associated with the same QTL. These are novel QTL for EAH, in that none of these SNPs coincide with previously reported QTL for similar traits ,32. The There were also many instances of agreement in location between the regions bearing putative QTL in the commercial breeder lines analyzed in the current study and other, independent populations. Three SNPs were significantly associated with E3 on GGA1 and 4 in the current study. The SNP at 8.2 Mb on GGA4 is in the region where significant associations between microsatellite markers and other egg traits  were found in a cross between Rhode Island Red (RIR) and Green-legged Partrigenous (GLP), a native Polish breed . In thisThree SNPs in the current study were significantly associated with EPD, on GGA1, 7 and 20. The SNP on GGA1 at 58.6 Mb is located in the region where a suggestive QTL for egg production was detected in a RIR × WL cross  and a CoThree overlapping 3-SNP windows were significantly associated with EPS on GGA7. These windows are likely associated with the same QTL in the region where suggestive QTL for egg shell thickness and egg shell strength at 34 wk were detected in a RIR× WL cross . A suggeSix SNPs, on GGA1, 4, 11 and 13, and two 3-SNP windows, on GGA1 and 25, were significantly associated with EYW. The SNP on GGA1 at 24.7 Mb coincides with a QTL region for egg weight at 29 wk in a RJF × WL cross . The SNPThree SNPs, on GGA3, 5 and 23, and one 3-SNP window, on GGA19, were significantly associated with LAH. The SNP at 77.3 on GGA3 is located in a region where a parent-of-origin effect QTL with paternal expression for egg weight at 41–60 wk was detected in a RIR × WL cross . Note thTwo SNPs were significantly associated with LPD, on GGA3 and 24. The SNP on GGA3 at 44.8 Mb coincides with a previously reported suggestive QTL for overall egg production (number of eggs at 16–55 wk) in a layer × broiler cross .Two SNPs were significantly associated with LYW, on GGA1 (previously discussed) and GGA2. The SNP on GGA2 at 28.4 is close to the QTL location for egg weight at 41–60 wk in a RIR × WL cross .Our citing of results from studies using different breeds in the previous, is to support the finding in terms of QTL location. This does not directly imply that SNPs found to be segregating for QTL in one population will work for another population. However, with higher correlations of LD between lines, one can expect that specific SNPs that have significant association with a trait in one line will also be useful in the other line.In summary, in the current study, we have demonstrated the preservation of short-distance LD across generations, an essential element of successful MAS. We have shown the general agreement in identification of SNPs associated with phenotype between use of SNP-wise and genome-wise association analyses. We have verified, in an elite layer breeding line, the QTL regions for several previously reported egg-production and egg-quality traits. We have also identified novel putative SNP-trait associations for egg-production and egg-quality traits in this elite layer line.The authors declare that they have no competing interests.BA managed the data, conducted SNP association and ASReml analyses, drafted the manuscript and was involved in development of algorithms for the BayesB and parentage test analyses. ES performed linkage disequilibrium analysis and drafted the LD sections of the manuscript. JA, PS, JEF and NPO managed the experimental birds, and the collection and in-kind contribution of phenotypic and pedigree data and blood samples for genotyping. AH was involved in development of SAS programs for mixed model analysis. DH was involved in development of algorithms for the BayesB analysis. RLF provided advice and programs for the BayesB and parentage test analyses and advice for C++ programming. JCMD and SJL conceived the study and were responsible for its overall design, management, and communications; secured extramural financial support for the project; and edited the manuscript. All authors read and approved the final manuscript.SNPs identified to be associated with traits in Line 1.Click here for file


Pinus pinaster Ait.), a conifer characterized by a huge genome size (∼23.8 Gb/C).There is considerable interest in the high-throughput discovery and genotyping of single nucleotide polymorphisms (SNPs) to accelerate genetic mapping and enable association studies. This study provides an assessment of EST-derived and resequencing-derived SNP quality in maritime pine (in vitro SNPs), chosen on the basis of functionality scores, presence of neighboring polymorphisms, minor allele frequencies and linkage disequilibrium and ii/ 200 SNPs screened from ESTs (in silico SNPs) selected based on the number of ESTs used for SNP detection, the SNP minor allele frequency and the quality of SNP flanking sequences. The global success rate of the assay was 66.9%, and a conversion rate (considering only polymorphic SNPs) of 51% was achieved. In vitro SNPs showed significantly higher genotyping-success and conversion rates than in silico SNPs . The reproducibility was 100%, and the genotyping error rate very low .A 384-SNPs GoldenGate genotyping array was built from i/ 184 SNPs originally detected in a set of 40 re-sequenced candidate genes (in silico SNPs are counterbalanced by a lower conversion rate than in vitro SNPs. This drawback is acceptable for population-based experiments, but could be dramatic in experiments involving samples from narrow genetic backgrounds. In addition, we showed that both the visual inspection of genotyping clusters and the estimation of a per SNP error rate should help identify markers that are not suitable to the GoldenGate technology in species characterized by a large and complex genome.This study demonstrates that ESTs provide a resource for SNP identification in non-model species, which do not require any additional bench work and little bio-informatics analysis. However, the time and cost benefits of  In the last few years, the development of high-throughput methods for the detection and genotyping of single nucleotide polymorphisms (SNPs) has led to a revolution in their use as molecular markers in vitro or in silico approaches. In vitro methods, such as the re-sequencing of targeted amplicons, are generally more appropriate when sequence data is limited or when one is interested in polymorphisms in specific genotypes or candidate genes. This approach is generally costly and time consuming, but has been proven successful to detect SNPs in many organisms (reviewed by in silico discovery is the most obvious method for de novo SNP identification. Although this approach mainly provides markers located in transcribed regions (mostly coding and 3′UTR), it offers a low cost source of abundant SNPs and has been validated by large scale genotyping for a number of plant species including Arabidopsisin silico SNPs varies depending on the assembly depth, the range of tissues considered, the diversity of the target species, but also on how well this diversity is represented within the database in silico SNPs available will thus differ considerably between species, although a global trend towards more SNPs for more ESTs from different tissues is expected for species with similar diversity. For example, about 9,000 high quality SNPs were detected in a first catfish assembly comprising 54,960 ESTs in silico SNPs is then possible, as shown by Wanget al.In non-model species, large scale SNP genotyping involves two main steps: first the discovery of polymorphisms, and second the genotyping of a set of specimens. SNP identification can proceed either from avyet al.ckertet al.There is no one ideal method for SNP genotyping and the selection of an appropriate technique largely depends on many factors including cost, accuracy, multiplexing capacity and throughput, equipment and difficulty of assay development Pinus pinaster Ait.) genome is extremely large  P. pinaster expressed sequence tags (ESTs) were produced in the past decade, followed by the re-sequencing of more than 40 wood-quality and drought-stress related candidate genes P. pinaster. Our objectives were three-fold: i/ validate a number of SNPs for future linkage mapping and candidate-gene-based association studies, and ii/ compare the conversion rate of SNPs derived from in vitro versus in silico datasets, as to our knowledge no other study in conifers has attempted to genotype a large number of in silico SNPs without preliminary re-sequencing, and iii/ estimate the genotyping error rate of the GoldenGate technology for a conifer genome, which has not been reported so far. The SNPs validated in this study have been made available through the NCBI database , 210 offspring resulting from open-pollinated or controlled crosses among the G0 trees , 29 trees randomly sampled in the same geographical area as the G0 trees, and 5 trees involved in two- and three-generation outbreed pedigrees, used for linkage and QTL mapping. DNA was extracted from needles using Invisorb® Spin Plant Mini Kit , and quantified with a Nanodrop ND-1000 spectrophotometer .in vitro SNPs. The second sequence dataset consisted in a collection of 26,476 maritime pine ESTs assembled in 3,995 non-singleton contigs and 7003 singletons (unigene available online at http://cbi.labri.fr/outils/SAM2/COMPLETE/ under the project name “Pinus pinaster 14_02_2007”). These ESTs were derived from six different libraries constructed using different tissues, and a number of segregating haploid genomes from 3 up to 300 from different provenances  or drought stress resistance . For eacvenances . We usedsnp2illumina, for automatically extracting SNPs from multifasta sequence files and output them as a SequenceList file compatible with the Illumina Assay Design Tool software (available online at http://www.illumina.com). This file contains the SNP names and surrounding sequences with polymorphic loci indicated by IUPAC codes for degenerated bases. The Perl script snp2illumina can work in batch mode and is available upon request from the corresponding author.We developed a Perl script, The functionality score provided by Assay Design Tool software is similar to a predicted probability of genotyping success, taking into account the sequence conformation around the SNP, the lack of repetitive elements in the surrounding sequence, and in the case of model species the sequence redundancy against the available sequence database in vitro SNPs, our objective was to include as many polymorphisms as possible for each gene fragment so depth of coverage was preferred. For in silico SNPs, our goal was to include a low number of markers per unigene in a large number of unigenes, thus giving more emphasis to breath of coverage. The main technical constraint for selecting in vitro SNPs was that the selected polymorphisms should not be less than 60 nucleotides away from each other. When several SNPs stood within this limit it was decided to filter out lowest frequency variants and polymorphisms showing high level of linkage disequilibrium with other selected SNPs of the same fragment. Rare variants  were also discarded. To select in silico SNPs we used the log-file of the snp2illumina script that records for each SNP the number of ESTs considered for the detection, the minor allele frequency (MAF) and the PolyBayes score. To minimize the number of false positives we included in the assay only SNPs with a PolyBayes score above 99%, with either a minor allele appearing at least twice within four to ten ESTs, or a MAF above 20% when more than ten ESTs were available. Indeed, it is highly unlikely that sequencing errors of two independently sequenced ESTs occur at the same base location. We also excluded SNPs that were surrounded by other polymorphisms in the immediate 60 bases to avoid technical problems due to neighboring polymorphisms. In both cases, chromatograms were visually checked to ensure the quality of the flanking sequences, and we used BLASTN analysis in vitro and in silico SNPs belonged to different genes.Two contrasted strategies “depth vs. breath of SNP coverage” were adopted to select informative SNPs. In respect to i.e. when the homozygous clusters normalized theta values were not in the  or  ranges, as illustrated in The Illumina GoldenGate technology  was used to carry out the genotyping reactions in accordance with the manufacturer's protocol aunderset al.aoet al.MIP) in a family in which one or both parents and m children have been genotyped can be derived from the marker allele frequency p in the studied population, m and Π as follows MIP over all families and averaging Π over all SNPs. This procedure allows to estimate a per SNP as well as a global genotyping error rate p) estimated on the Aquitaine G0 genotyping dataset (212 samples).We used the breeding population pedigree information (relationships between first and second generation) to detect possible Mendelian Inconsistencies (MIs) between parents and offspring using the PedCheck software in vitro SNPs were detected in the dataset of re-sequenced fragments. Overall 155, 81 and 28 SNPs were discarded because of low functionality scores, neighboring polymorphisms, or because they corresponded to rare variants, respectively. The 184 remaining SNPs included in the assay represented 40 different gene fragments  divided by the total number of SNPs in the assay, and second the conversion rate, which is the number of polymorphic SNPs divided by the total number of SNPs in the assay, as defined in Fin vitro SNPs generally showed significantly higher genotyping-success and conversion rates compared to in silico SNPs .The mean call rate, which is 1 minus the rate of missing data, exceeded 98% at the SNP level and ranged from 73.4% to 93.5% at the sample level for four of the five plates analyzed. It dropped to 77.5% at the SNP level and ranged from 13.8% to 87.5% at the sample level for the fifth plate where we noticed evaporation problems during the genotyping reactions. We found significant differences depending on the origin of the markers: in vitro- and in silico SNPs is shown in in silico SNPs compared to in vitro SNPs . Most of the 22 monomorphic in vitro SNPs corresponded to either SNPs that were monomorphic in the Aquitaine sequences (10 SNPs), rare variants (3 SNPs with a MAF below 5% in the Aquitaine sequencing dataset), or were detected on alignments that did not include any sequences from south-western France (3 SNPs). Among the polymorphic SNPs, 35.7% of in vitro and 29.4% of in silico SNPs corresponded to rare variants (MAF ≤10%)  .2-test P-value of 0.0019), as found in Pavyet al.Prior to the construction of the SNP bead array, a functionality score was calculated for each candidate SNP using the Illumina Assay Design Tool. The higher the score, the more likely will the SNP be successfully genotyped. We could not genotype any of the five SNPs with functionality scores below 0.5, and only 13 of the 27 SNPs with functionality scores between 0.5 and 0.6 . SNPs wiin vitro SNPs of the genotyping assay, 101 were previously identified in alignments containing 10 sequences or more from the Aquitaine population and were used to assess the reliability of allele frequency estimates based on sequencing data. The correlation between marker allele frequencies determined by sequencing and genotyping was ∼0.83 (considering only the 212 unrelated samples from the Aquitaine G0 breeding population) (Among the 112 polymorphic ulation) , showing2-test P-value of 0.51, see also per SNP genotyping error rate. Among the seven remaining markers, three showed error rates ranging from 2.9% to 3.3%, and four (two in vitro SNPs and two in silico SNPs) showed particularly elevated error rates (Π between 16% and 70%). In these cases (distribution of error rates skewed owing to four SNPs with very high error rates), the estimate of the mean error rate tends to be biased upwards For 84 and 81 offsprings of the G1 population, either one or both parental G0 trees were genotyped in the assay. This dataset consisted in 36,991 genotyping datapoints corresponding to 222 samples (165 G1 and 57 G0 trees) genotyped for 188 polymorphic SNPs, after excluding 4,745 missing data. We found a total of 181 Mendelian Inconsistencies (MIs). Most of these errors (75%) appeared in only nine parents-offspring pairs for which the MI rate ranged from 4% to 17%, suggesting laboratory errors  rather than genotyping errors. In six cases we assumed that the MIs originated from the offspring genotypes as the parents were involved in other crosses where no MI was found. For the other cases we could not tell parents and offspring MIs apart. Setting aside these possible human errors, 46 MIs were detected for 35,521 genotyping datapoints. MIs were not significantly more abundant for samples presenting low call rates  and ii/ 9,364 SNPs screened from ESTs (in silico SNPs). Two different SNP selection strategies were followed, “depth vs. breath of SNP coverage”. For in vitro SNPs we aimed at validating as many polymorphisms as technically possible for each fragment (depth), whereas for in silico SNPs we aimed at validating few SNPs per unigene in a large number of unigenes (breath). A total of 184 in vitro SNPs were chosen on the basis of functionality scores, presence of neighboring polymorphisms, MAF and linkage disequilibrium. Moreover, 200 in silico SNPs were selected based on three parameters that proved critical for high validation rate of EST-derived SNPs In vitro SNPs showed significantly higher genotyping success  and conversion  rates than in silico SNPs. The functionality score estimated for each SNP, which in our case could not account for sequence redundancy in the genome, showed a significant relationship with success of genotyping. The reproducibility of the assay was very good , and the genotyping error rate very low .A 384-SNPs GoldenGate genotyping array for P. pinaster: 66.9% of the SNPs were translated into easily interpreted genotypic clusters. This success rate is similar to that observed for Pinus taeda , but lower than that observed for Picea glauca or Picea mariana . So far, two main causes have been invoked in the literature for explaining genotyping failures in GoldenGate assays for non-model species. First, the partial knowledge of large and redundant genomes can be a limiting factor to design an efficient SNP genotyping assay. Indeed, flanking sequences cannot be fully validated for locus specificity and the possible presence of repetitive elements Pinus pinaster, both hypotheses can be examined: we reached a 79.6% success rate when considering a group of 103 in vitro SNPs that were detected on more than 30 individuals from the Aquitaine population, which is similar to that observed in Picea species . The rate dropped to 55.8% for another group of 43 in vitro SNPs detected on 10 to 30 samples. We checked that this difference in success rates was not due to differences between allelic frequency distributions in both groups (data not shown). This significant difference  suggests that the sample size of the SNP discovery panel has a large impact on the conversion rate. However, the high conversion rate achieved using SNPs from well characterized DNA regions (79.6%) still does not reach that reported for human . As discussed in Pavyet al.Data obtained from the GoldenGate assay reported in this paper suggest that the bead array technology is suitable for the complex and large genome of in vitro SNP and in silico SNP conversion rates, a lower rate being observed for in silico SNPs. According to Wanget al.anget al.in silico SNPs detected on at least four sequences and with a minor allele present twice, against a rate of 33.3% for SNPs detected on four or fewer sequences with minor allele present only once. In our case, chromatograms have also been checked to ensure high-quality of flanking sequences for primer design, but the presence of undetected polymorphisms in these regions is likely as most SNPs were detected on only ten ESTs or less  and in silico (discovered from mRNA sequences) SNPs. We previously defined the conversion rate as the number of polymorphic SNPs divided by the total number of SNPs in the assay. Since monomorphic loci were twice more abundant for in silico SNPs than for in vitro SNPs, this also partly explains their lower conversion rate. Indeed, the EST database used for in silico SNP detection included sequences from samples of various origins . This information not being available upfront in the unigene that we used for in silico SNPs discovery, we had overlooked its influence initially, but have been integrating it in future studies.We found a significant difference between  or less . We coule region , i.e. thin vitro SNPs were found monomorphic on the genotyped trees, while they were detected as polymorphic loci with intermediate frequency estimates in the re-sequenced haploid panel from the Aquitaine population. Given that we are confident that they were not sequencing artifacts, this observation could be explained by either the lack of amplification of one allele due to polymorphism in the priming site, the presence of gametophyte selection against deleterious mutations (as sequences were obtained from haploid megagametophytes while genotyping was performed on diploid DNA), or the general complexity of the pine genome as previously discussed. In the latter case, the distinction between genotyping reaction failures and monomorphic SNPs is not obvious. In this study we decided to discard nine monomorphic SNPs with acceptable GenTrain scores but showing either subgroups in the homozygous cluster, or normalized theta values departing from the classical 0/1 values for an homozygous locus. These patterns might be particular forms of cluster compression , but could be dramatic in experiments involving samples from narrow genetic backgrounds. For example, Eckertet al.P. taeda mapping pedigree, using in vitro SNPs from a database that did not include any sequences of the parental lines of the mapping population. In addition, we showed that both the visual inspection of genotyping clusters and the estimation of a per SNP error rate should help identify markers that are not suitable to the GoldenGate technology in species characterized by a large and complex genome.In this study, we demonstrated that ESTs provide a resource for SNP identification in non-model species, which do not require any additional bench work and little bioinformatics analysis. However, the time and cost benefits of in vitro SNPs, including 560 SNPs detected from re-sequenced amplicons provided by David Neale, UC Davis, CA, USA, http://dendrome.ucdavis.edu/crsp/, and 710 in silico SNPs selected with the same criteria as in this study). This second generation SNP-array will be used to establish a species consensus map based on the analysis of seven pedigrees, and for association mapping for a series of traits  measured on clonal and progeny tests on the first and second breeding populations.Recently, a larger-scale SNP-array was designed for maritime pine, comprising 1,536 SNPs Click here for additional data file.Table S1in vitro and in silico SNPs that were polymorphic in the assay.NCBI ss accession numbers for (0.03 MB XLS)Click here for additional data file.Table S2in vitro SNPs detection and associated projects.List of the 41 candidate genes used for (0.03 MB XLS)Click here for additional data file.Table S3in vitro SNPs and their frequencies in the total sequencing dataset, in the Aquitaine sequencing dataset and in the genotyped samples.List of the 184 (0.06 MB XLS)Click here for additional data file.Table S4Call rate classes of the genotyped samples.(0.02 MB XLS)Click here for additional data file.


KIT, MITF, and a locus on chromosome 8, together explain 24% of the variation of proportion of black. However, a surprisingly large number of loci of small effect are necessary to capture the remaining variation. A second trait, fat concentration in milk, had one locus of large effect and a host of loci with very small effects. Both these distributions of effects were in contrast to that for a third trait, an index of scores for a number of aspects of cow confirmation , which had only loci of small effect. The differences in distribution of effects among the three traits were quantified by estimating the distribution of variance explained by chromosome segments containing 50 SNPs. This approach was taken to account for the imperfect linkage disequilibrium between the SNPs and the QTL affecting the traits. We also show that the accuracy of predicting genetic values is higher for traits with a proportion of large effects (proportion black and fat percentage) than for a trait with no loci of large effect , provided the method of analysis takes advantage of the distribution of loci effects.Prediction of genetic merit using dense SNP genotypes can be used for estimation of breeding values for selection of livestock, crops, and forage species; for prediction of disease risk; and for forensics. The accuracy of these genomic predictions depends in part on the genetic architecture of the trait, in particular number of loci affecting the trait and distribution of their effects. Here we investigate the difference among three traits in distribution of effects and the consequences for the accuracy of genomic predictions. Proportion of black coat colour in Holstein cattle was used as one model complex trait. Three loci,  KIT, MITF, and a locus on chromosome 8; however a surprisingly large number of additional loci, all of small effect, are required to capture the remaining variation. For overall type, a very large number of loci are necessary to capture the same level of variance. We also show that the accuracy of predicting genetic values is higher for traits with a proportion of large effects (proportion black and fat percentage) than for a trait with no loci of large effect , provided the method of analysis takes advantage of the distribution of loci effects.Prediction of future phenotypes or genetic merit using high-density SNP chips can be used for prediction of disease risk in humans, for forensics, and for selection of livestock, crops, and forage species. Key questions are how accurately these predictions can be made and on what parameters does the accuracy depend. In this paper, we use three dairy cow traits—proportion of black on coat, fat percentage in milk, and overall type, which measures cow confirmation—to demonstrate the large differences among genetic architectures of complex traits. For example 24% of the genetic variance in proportion of black is determined by three loci,  Genomic prediction of future phenotypes or genetic merit using dense SNP genotypes can be used for prediction of disease risk, for forensics, and for estimation of breeding values for use in selection of livestock, crops and forage species The accuracy of genomic predictions will depend on the number of phenotypes used to derive the prediction equation, the heritability of the trait, the effective population size, the size of the genome, the density of markers, and the genetic architecture of the trait, in particular number of loci affecting the trait and distribution of their effects MITF) KIT) gene is associated with tobiano white-spotting pattern KIT gene are associated with white coat colour phenotypes KIT are also associated with roan coat KIT gene have been characterised and associations with dominant white colour demonstrated KIT and MITF loci respectively Coat colour in mammals is usually regarded as trait controlled by a few loci of large effect. However, aspects of coat colour have been suggested as a model for investigating complex trait architecture, given the close relationship between genotype and phenotype DGAT1 gene accounts for 30% of the variation in fat% from Holstein Friesian cattle Complex traits which have been studied by GWAS in dairy cattle include fat% and “type”, a complex conformation trait KIT, MITF and a locus on chromosome 8 together explain a considerable proportion of the variation in proportion of black, but a large number of loci of small effect are necessary to capture the remaining variation. We then contrast the accuracy of genomic prediction which can be achieved for this trait with the accuracy of genomic predictions for overall type and fat% in milk. The results demonstrate a clear effect of trait architecture on the accuracy of genomic predictions.In this paper, we use proportion of black on the coat, fat% and overall type to show that differences in the distribution of loci effects are recognisable using a new method to estimate the distribution of variance explained by each QTL. We demonstrate that three loci, −4 in the discovery population. We tested these in an independent validation population and confirmed three SNPs at P<0.001, KIT locus on chromosome 6 . There was another highly significant SNP at 32,459,763 bp on chromosome 22 which is in very close proximity to the MITF locus . There was also a highly significant SNP on chromosome 8 at 64,164,842bp. This SNP is within the zinc finger CCHC domain containing 7 gene (ZCCHC7). However zinc finger CCHC domain containing genes have not been implicated in coat colour development in any species. Perhaps a more plausible candidate in this region is PAX5 . Other members of this family, PAX3 and PAX6, have been demonstrated to interact with MITFPAX5 should be nearly identical to PAX6, because their C-terminal subdomains are 75% identical and all DNA-contacting residues are conserved PAX5 and MITF remains to be demonstrated.While GWAS results for fat% have been reported previously, no GWAS results have been reported for proportion of black KIT, MITF and the locus on chromosome 8 when we fitted models evaluating these effects.Together the three loci on chromosomes 6, 8 and 22 accounted for 24% of the variation in proportion of black phenotypes in the validation population. There was no evidence of dominance for any of these significant SNPs, and no statistical support for an interaction between the significant SNPs in KIT gene has the largest F-value, exceeding the next largest SNP by 10 F units. However there are significant SNPs extending 10-15Mb either side of the most significant SNP. This is likely to be caused by the pattern of linkage disequilibrium in livestock: while at short distances levels of r2 between markers are similar to that observed in humans, low levels of LD (r2≤0.1) extend for many Mb in Holstein-Friesian cattle, probably due to recent reduction in effective population size DGAT1 gene, and other large effects on chromosomes 2, 6 and 20. For overall type, a small number of SNPs had validated associations, however the false discovery rate in the validation population was close to 100% , on chromosome 5  and 20  for fat%. However there were no effects greater than 5×10−5 phenotypic standard deviations for overall type.To overcome the tendency to find significant SNPs up to 15 Mb from a causal variant, we then used a different approach to conduct the genome wide association studies, where all SNPs were fitted simultaneously as random effects sampled from a t-distribution  between adjacent SNPs in our population was only 0.271. Therefore we took the approach of using chromosome segments to derive the distribution of effects as chromosome segments with multiple SNPs are more likely to capture the complete effect of the QTL. A chromosome segment was defined as consisting of 50 adjacent SNP loci. The SNPs were approximately equally spaced, such that a 50 SNP segment was 3350kb long. This size of segment was chosen as a compromise between having too little SNP information to accurately estimate its contribution to the variance, and having sufficiently small segments to enable interpretation regarding the distribution of effects on the trait. Then a genomic relationship matrix among the animals for that chromosome segment was constructed .However, estimates of proportion of variance explained derived in this way contain sampling error. For instance, even if a chromosome segment has no effect on the trait, the estimated variance explained can be positive . This was reflected in the fact that the sum of the variances across the segments without correction for sampling error was greater than the total genetic variance. We wish to estimate the distribution of the true effects of chromosome segments rather than the distribution of estimated effects. To do this we used permutation to derive the distribution of the proportion of variance explained due to the sampling error alone. Then we used maximum likelihood to estimate the distribution of true effects  which, wKIT, MITF and the locus on chromosome 8 were set to zero so variance caused by these mutations was not double counted. The same procedure was used for the segments surrounding the DGAT1 gene and other large effects for fat%. For proportion of black and particularly fat%, a small proportion of segments are necessary to capture a significant proportion of the variance, while for overall type a greater number of segments are required. Note that the sum of the variance from the segments explaining the largest proportion of the variance is now 20%, compared with 24% estimated from the GWAS. This reflects the fact that the estimate of variance explained is regressed to account for estimation error. For proportion of black, as segments other than the three containing KIT, MITF and the significant SNP on chromosome 8 only explain a small proportion of the variance, many of them are required to explain even the majority of the variance.The distribution of variances of chromosome segments can also be expressed as the cumulative proportion of the total variance explained when the segments are ranked from largest variance to smallest . The varX is a matrix with a row for each animal and a column for each SNP and Xij is the number of “2” alleles where they alleles are designated 1 or 2, To investigate the effect of the distributions of loci effects on the accuracy of genomic estimated breeding values, we used SNP effects for each trait from the Bayesian approach described above to predict genomic estimated breeding values. This was done for the independent validation population of 400 bulls, as GEBV was also calculated using subsets of SNPs ranked in order of the size of their effect. For each subset, BayesA was re-run to predict SNP effects. For proportion of black, a very small number of SNPs were required to achieve close to 95% of the accuracy possible with the full set of SNPs, while at the other extreme for overall type 2000 SNPs were required to achieve greater than 90% of the accuracy possible with the full set of SNPs, For traits with a few moderate effects, and many small effects, such as proportion of black, the accuracy of estimating the moderate effects will be much higher than the accuracy of estimating the very small effects. There is also a large effect of the number of records used to estimate the effects – for proportion of black there were only 327 records while for the other traits there were 756 records. When the estimated effects are used in a prediction equation for estimated breeding values, the moderate effects therefore contribute the overwhelming majority of the total accuracy of prediction. With a small number of phenotypic records, the estimates of segments with small effects can be so inaccurate that they contribute nothing to the accuracy of prediction. This explains the apparent discrepancy between Our results demonstrate that large differences exist in the architecture of different complex traits. For both proportion of black and fat% there are segregating mutations of moderate effect so that the distribution of effects is leptokurtotic. This in contrast to overall type which has only loci of small effect, and the distribution of these effects could be assumed to be normal.2), the length of the genome (L), and the distribution of QTL effects. The distribution of effects could be either normal or leptokurtotic. When a normal distribution of effects is assumed, the accuracy of genomic breeding values can be predicted as a = 1+2 λ/N, and 2λ = qk/h, with e)k = 1/log. For portion of black, each bull himself was scored according to the proportion of black on the entire body, from 0% to 100% black. The values ranged from 5% black to 95% black. The scorer was the same for all the bulls.The data set consisted of 1200 Australian Holstein bulls. For fat% and overall type the ‘phenotype’ used for each bull was the mean phenotype of his daughters. To obtain this phenotype we de-regressed the Australian breeding values (ABVs) to remove the contribution from relatives other than daughters The bulls were genotyped for the Illumina Bovine50K array, which includes 54,001 Single Nucleotide Polymorphism (SNP) markers Criteria for selecting SNPs were; less than 5% pedigree discordants , 90% call rate, MAF>2%, Hardy Weinberg P<0.00001. 40077 SNPs met all of these criteria. A small number of these were not assigned to any chromosome on Bovine Genome Build 4.0, and were omitted from the final data set, as were SNPs on the X chromosome. Parentage checking was then performed again, and any genotypes incompatible with pedigree were set to missing.To impute missing genotypes, the SNPs were ordered by chromosome position. All SNPs which could not be mapped or were on the X chromosome were excluded from the final data set, leaving 39,048 SNPs. To impute missing genotypes, the genotype calls and missing genotype information was submitted to fastPHASE chromosome by chromosome The discovery dataset consisted of bulls progeny tested before 2004 (n = 756). For proportion of black portion 327 bulls in the reference set had phenotypes. The bulls in the validation dataset were progeny tested during or after 2004 (n = 400)y is a vector of proportion of black, μ is the mean, S is the (random) effect of the sire of each bull, x is a vector of genotypes, b is the effect of the SNP, and e is a vector of random residuals. The variance of the sire effects was Iσ2S where I is an identity matrix and σ2S is the sire variance. Fitting the sire effect should remove any spurious associations due to family structure. All data analyses were performed using mixed linear models with variance components estimated by residual maximum likelihood In the discovery set of bulls, a linear model was fitted to the bull's proportion of black phenotypes to determine if the SNPs accounted for any variation. The top–bottom called genotypes were re-coded as 0 for the homozygote of the first alphabetical allele, 1 for the heterozygote, and 2 for the homozygote of the second alphabetical allele. The effect of each SNP was estimated in turn using the model G) estimated from DNA markers in the BLUP equations. y is a vector of phenotypes, μ is the mean, n1 is a vector of 1s, Z is a design matrix allocating records to estimated breeding values, g is a vector of breeding values and e is a vector of random normal deviates ∼g can be modelled by the combined effects of all the SNPs g = Wu where ju is the effect of the thj SNP, and W are ijw for the thi animal and thj SNP, where ijw = 0−2jp if the animal is homozygous 11 at the thj SNP, 1−2jp if the animal is heterozygous and 2−2jp if the animal is homozygous 22 at the thj SNP, where jp is the allele frequency of the 1 allele of the SNP. The diagonal elements of WW′ will be m is the number of SNPs. If WW′ is scaled such that If there are many QTLs whose effects are normally distributed with constant variance, then genomic selection is equivalent to replacing the expected relationship matrix with the realised or genomic relationship matrix /h where yval was the phenotype , for bulls in the validation set, and h is the correlation between the phenotype and the true breeding value estimated, (the square root of the heritability of the records was used).The realised accuracy of GEBV was calculated as r a design matrix allocating records to the marker effects with element Xij = 0, 1 or 2 if the genotype of animal i at SNP j is 11, 12 or 22 respectively. u is a (m×1) vector of SNP effects assumed normally distributed e is a vector of random deviates where iv is the polygenic breeding value of the thi animal, with variance A is the numerator relationship matrix derived from pedigrees. In BayesA the prior for A Bayesian approach to simultaneously predicting the effect of all SNPs to derive the prediction equation was used, namely BayesA 1G), and a second genomic relationship matrix (2G) using all SNPs except those in the current 50 SNP segment. We the fitted the modely is a vector of phenotypes, μ is the mean, n1 is a vector of 1s, Z is a design matrix allocating records to animals, 1g is a vector of genetic effects for a 50 SNP segment, assumed to be normally distributed with mean 0 and co(variance) 2g is a vector of breeding values based on all the other segments, assumed to be normally distributed with mean 0 and co(variance) e is a vector of random normal deviates ∼For each 50SNP segment of chromosome, we estimated the proportion of variance explained by building a genomic relationship matrix (as described above) based on the 50SNPs only . Therefore the square roots of these estimates were assumed to be near-zero  and the positive half of a normal distribution the other half. The standard deviation of e, σ, was then taken as the square root of the average proportion of variance explained multiplied by 2 (the multiplication by two was to account for the fact that negative estimates of the proportion of variances explained are reported as zero).The estimate of the proportion of variance explained by a chromosome segment i2t) given that we had a sample of estimated chromosome segment variances .We then used maximum likelihood to estimate the distribution of true chromosome segment variances . We then estimate the frequency of these discrete values. The probability of observing j and σ was taken as t+e is negative for a segment then 2y would be reported as zero since negative variances are not allowed).We estimate the distribution of jf. The EM algorithm had three stepsThen an expectation maximisation (EM) algorithm was used to estimate the proportion of chromosome segments in each class jf. to 0.01.Initialise each i was Calculate the probability of the j given the yUpdate the proportion of chromosome segments in each class as jf values did not change between iterations. The results Click here for additional data file.