Genetic Correlation of Two Traits using GBLUP

Performing GBLUP Analysis

The Genetic Correlation of Two Traits using GBLUP method performs a bivariate REML analysis on two selected traits to estimate the genetic variance of each trait and the genetic covariance between two traits that can be captured by all SNPs.

Note

This method uses (with a genotypic spreadsheet) or assumes (with a numerically recoded spreadsheet) an additive genetic model.

Options

gblupDialog

Compute Genomic BLUP (GBLUP) Dialog Window

  • Computations: The default computation for each bivariate-algorithm relationship matrix includes the residual covariance.

    • Exclude the residual covariance (may help convergence): Checking this box may help algorithm convergence by excluding residual covariance in subsequent computations.
  • Impute missing genotypic data as: Missing genotypic data can be imputed by either of the following methods:

    • Homozygous major allele: All missing genotypic data will be recoded to 0.

    • Numerically as average value: All missing genotypic data will be recoded to the average of all non-missing genotype calls (using the additive model).

      Note

      If Correct for Gender (see below) is also selected, and there is non-missing data for both males and females in a given marker, averages for males and females will be computed and used separately.

  • Correct for Gender: Assumes the column is coded as if the male were homozygous for the X-Chromosome allele in question. Uses the [Taylor2013] gender-correction algorithm (see Correcting for Gender). Two values of the ASE are output, one for each gender.

    • Choose Sex Column: Choose the spreadsheet column that specifies the gender of the sample. This column may either be categorical (“M” vs. “F”) or binary (0 = male, 1 = female).
    • Chromosome that is hemizygous for males: Usually the X Chromosome, which is the default.
  • Use Pre-Computed Genomic Relationship Matrix: To use, check this option, then click on Select Sheet and select the genomic relationship matrix spreadsheet from the window that is presented. To be valid, this spreadsheet must follow the rules outlined in Precomputed Kinship Matrix Option.

    Note

    When using a pre-computed genomic relationship matrix, the matrix M and the HWE variance sum \phi are re-calculated from the genotypic data being used for this analysis.

  • Correct for Additional Covariates: Allows additional fixed effects to be added to this model from columns of this spreadsheet. Fixed effect covariates can be binary, integer, real-valued, categorical or (if actual genotypic data rather than recoded genotypic data is being used for the analysis) genotypic. In all cases, if a marker is used as an additional fixed effect, it will not be included in the analysis in any other way. To begin, check this option, then click on Add Columns to get a choice of spreadsheet columns to use.

  • Missing Phenotypes: To predict random effects (genomic merit/genomic breeding values) and the phenotypes for samples with missing phenotypes, select Predict random effects for samples with missing phenotypes. Selecting this will also include samples with missing phenotypes as a part of the basis for the ASE calculations. Otherwise, select Drop samples with missing phenotypes.

GBLUP Correlation Output

Go see Genomic Best Linear Unbiased Predictors Analysis for details on this output. Correlation statistics match those of the GCTA bivariate-analysis method. For more information see: GCTA bivariate GREML analysis.