# Leibniz MMS Days 2017 - Abstract

**Wittenburg, Dörte**

*Covariance between genotypic effects and its use for genomic inference in half-sib families*

In livestock, current statistical approaches utilise extensive molecular data, e.g. SNPs, to improve the genetic evaluation of individuals. The number of model parameters increases with the number of SNPs, so the multicollinearity between covariates can affect the results of whole genome regression methods. In this study, dependencies between SNPs due to linkage and linkage disequilibrium among the chromosome segments were explicitly considered in methods to estimate SNP effects. The population structure affects the extent of such dependencies, so the covariance among SNP genotypes was derived for half-sib families. The covariance matrix was used to specify prior assumptions for SNP effects in a Bayesian framework. The approach was applied to simulated and semi-real data to identify genome segments that affect performance traits and to investigate the impact on predictive ability. The inclusion of dependence is particularly important for genomic inference based on small sample sizes.