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For an association with the trait. The associationPLOS Genetics | www.plosgenetics.orgbetween every SNP and each of your traits was assessed by a regression analysis employing the ASReml computer software [45]. The model utilized was exactly the same as for estimating heritability, but SNPi (SNPi, i = 1, two, 3, … , 729068) was moreover fitted as a covariate one at time (trait , imply + fixed effects + SNPi + animal + error). The model made use of to analyse the traits consistently incorporated dataset, breed, cohort and sex as fixed effects. Other fixed effects varied by trait. The fixed effects were fitted as nested within a dataset. Further details in the models utilized within the analysis are reported by Johnston et al. [46], Reverter et al. [47], Robinson and Oddy [48], Barwick et al. [49], Wolcott et al. [50], Bolormaa et al. [8], and Zhang et al. [44].Multi-trait meta-analysis chi-squared statisticWe applied a new statistic to discover the significance amount of SNPs in a multi-trait analysis. As a result, when the SNP effects on n different traits have been estimated indepen0 dently with no error covariance, the sum on the t2 (i.e., ti Iti , exactly where I is an identity matrix) would be distributed as a chi-squared with n degrees of freedom. Our approximate evaluation would create precisely this test statistic if the t values for different traits had no error covariance. If the t values for different traits had an error (co)variance matrix D, then the correct test statistic will be 0 t D{1 t distributed as a chi-squared with n degrees of freedom. We approximate D by the correlation between the estimated SNP effects across the 729,068 SNPs. We assume that most SNPs have little or no effect on most traits, so most of the (co)variance between effects is error covariance. However, the SNPs that do have a real effect on a trait will inflate the variance of SNP effects above 1.0. Therefore we convert the covariance matrix of SNP effects (D) to a correlation matrix (V) because this returns the diagonal elements to 1.0 which we know is the correct error variance for t statistics. Although it is not proof of the method, perhaps we offer the following intuitive analysis. If the SNP effects on different traits were estimated in independent GWAS then the correlation of SNP effects would be low and VI and the test statistic would be the sum of independent chi-squares, as expected. On the other hand, if the SNP effects on different traits were estimated from the same individuals, then the correlation of error ISA-2011B cost variances would be driven mainly by the phenotypic correlations between the traits. In this case the correlation of SNP effects would also reflect these phenotypic correlations and the test statistic we use would be a good approximation of the correct test statistic.number of SNP that were significant at the P -value tested and T is the total number of SNP tested. Validation of SNP effects. To validate SNP effects in the multi-trait test, we developed a new approach that uses a linear index of traits that had maximum correlation with the SNP. This new approach was PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/20040487 carried out as follows: 1) Splitting data as reference and validation populations; 2) Predicting missing phenotypes using multiple regression approach; 3) Performing single-trait GWAS in the reference population to get the SNP effects based on only the reference population; 4) Calculating a linear index of 22 traits for each SNP, which had maximum association with the SNP in reference population; and 5) Validating SNP effects using GWAS t.

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