Accounting for population structure in selective cow genotyping strategies

Abstract

The objective of the present study was to investigate the impact of considering population structure in cow genotyping strategies over the accuracy and bias of genomic predictions. A small dairy cattle population was simulated to address these objectives. Based on four main traditional designs (random, top-yield, extreme-yield and top-accuracy cows), different numbers (1,000; 2,000 and 5,000) of cows were sampled and included in the reference population. Traditional designs were replicated considering or not population structure and compared among and with a reference population containing only bulls. The inclusion of cows increased accuracy in all scenarios compared with using only bulls. Scenarios accounting for population structure when choosing cows to the reference population slightly outperformed their traditional versions by yielding higher accuracy and lower bias in genomic predictions. Building a cow-based reference population from groups of related individuals considering the frequency of individuals from those same groups in the validation population yielded promising results with applications on selection for expensive- or difficult-to-measure traits. Methods here presented may be easily implemented in both new or already established breeding programs, as they improved prediction and reduced bias in genomic evaluations while demanding no additional costs.