Use of genetic markers offers the potential to directly assess genetic variance and its distribution between putative QTL and a polygenic background.
However, a numerical analysis using (7) and (8) shows that for the same value of f s, small effects contribute more to the total genetic variance as the distribution of effects gets narrower.
The third method, 'BayesB' makes prior assumptions on the amount of genetic variance and the distribution of gene effects, and breeding values are estimated from the data points by Bayesian methods.
Furthermore, three scenarios were defined (Table 2), all assuming moderate heritability, but differing with respect to the distribution of genetic variance; either equally distributed over genomic regions (Scenarios 1 and 2) or located in a single region ("one effective locus") only (Scenario 3).
The hierarchical distribution of genetic variance among and within populations, based on F-statistics, was assessed using an analysis of molecular variance (AMOVA) performed with ARLEQUIN 3.1.
Mark Blows (University of Queensland) focused on the complex distribution of genetic variance, because characteristics of pleiotropy will have large effects on multivariate response to selection.
The additive genetic coefficients of variation (CV) were calculated to estimate the genetic variation of PALP, HOOF, LOCO as: CV = genetic variance trait mean ∗ 100 Distribution of estimated breeding values (EBVs) and genetic trends for PALP and HOOF were produced.
Here, a model of a continuous genome was used, rather than a finite number of independent regions as by Ødegård and Meuwissen [ 11], and the calculations assumed a fairly even distribution of genetic variance along the genome.
We obtain analytical expressions for the stationary genetic variance as a function of the distribution of effects, mutation rate, and selection coefficient.
If the trait architecture is unknown e.g. for novel traits which only recently received attention in breeding, we suggest to inspect the distribution of the genetic variance explained by each chromosome for guiding model selection in WGP.
First, BayesC was implemented to provide an accurate estimate of the genetic and residual variance for each trait to be included in the BayesB model since BayesB is sensitive to the prior distribution of the genetic variance and an inaccurate estimate could impact the results.
