Genomic selection has transformed animal breeding by enabling early prediction of genetic merit using genomic information.
Genomic selection has transformed animal breeding by enabling early prediction of genetic merit using genomic information. These predictions, known as genomic estimated breeding values (GEBVs), are central to accelerating genetic gain. However, their predictive ability across generations is not guaranteed. In practice, GEBVs tend to be over-dispersed when SNP–QTL relationships change over time, leading to erosion of predictive ability and potential mis-ranking of selection candidates. This erosion undermines the fundamental goal of genomic selection which is to make breeding faster and have more reliable genetic progress.
The uncertainty of GEBVs arises because single nucleotide polymorphism (SNP) markers are assumed to remain stable representatives of the underlying quantitative trait loci (QTL) across generations and populations. In reality, this assumption is violated: long-distance linkage disequilibrium (LD) between SNPs and QTL decays over generations due to recombination, allele frequencies shift due to drift and selection, and there are non-additive gene actions. These processes alter SNP–QTL associations and reduce the persistence of estimates of SNP effects over time.
The aim of this project is to understand and quantify the causes of erosion in genomic predictive ability across generations in livestock and to develop strategies to improve the persistence of SNP effect estimates over time. This will first be investigated through simulation studies designed to reproduce the observed erosion and assess the influence of factors such as LD decay, genetic distance between reference and target populations and selection bias. Based on the insights from these simulations, improved genomic evaluation strategies will be developed and then validated using real livestock data with single-step genomic evaluation methods to assess their potential for improving long-term predictive ability and persistence across generations.
Contact persons: Sine Marie Jepsen (PhD student) (