Definition of metafounders based on population structure analysis

Abstract

Limitations of the concept of identity by descent in the presence of stratification within a breeding population may lead to an incomplete formulation of the conventional numerator relationship matrix ( $$\mathbf{A}$$ ). Combining $$\mathbf{A}$$ with the genomic relationship matrix ( $$\mathbf{G}$$ ) in a single-step approach for genetic evaluation may cause inconsistencies that can be a source of bias in the resulting predictions. The objective of this study was to identify stratification using genomic data and to transfer this information to matrix $$\mathbf{A}$$ , to improve the compatibility of $$\mathbf{A}$$ and $$\mathbf{G}$$ . Using software to detect population stratification (ADMIXTURE), we developed an iterative approach. First, we identified 2 to 40 strata ( $$k$$ ) with ADMIXTURE, which we then introduced in a stepwise manner into matrix $$\mathbf{A}$$ , to generate matrix $${\mathbf{A}}^{{\varvec{\Gamma}}}$$ using the metafounder methodology. Improvements in consistency between matrix $$\mathbf{G}$$ and $${\mathbf{A}}^{{\varvec{\Gamma}}}$$ were evaluated by regression analysis and through the comparison of the overall mean and mean diagonal values of both matrices. The approach was tested on genotype and pedigree information of European and North American Brown Swiss animals (85,249). Analyses with ADMIXTURE were initially performed on the full set of genotypes (S1). In addition, we used an alternative dataset where we avoided sampling of closely related animals (S2). Results of the regression analyses of standard $$\mathbf{A}$$ on $$\mathbf{G}$$ were – 0.489, 0.780 and 0.647 for intercept, slope and fit of the regression. When analysing S1 data results of the regression for $${\mathbf{A}}^{{\varvec{\Gamma}}}$$ on $$\mathbf{G}$$ corresponding values were – 0.028, 1.087 and 0.807 for $$k$$ =7, while there was no clear optimum $$k$$ . Analyses of S2 gave a clear optimal $$k$$ =24, with − 0.020, 0.998 and 0.817 as results of the regression. For this $$k$$ differences in mean and mean diagonal values between both matrices were negligible. The derivation of hidden stratification information based on genotyped animals and its integration into $$\mathbf{A}$$ improved compatibility of the resulting $${\mathbf{A}}^{{\varvec{\Gamma}}}$$ and $$\mathbf{G}$$ considerably compared to the initial situation. In dairy breeding populations with large half-sib families as sub-structures it is necessary to balance the data when applying population structure analysis to obtain meaningful results.