Explorer Genomic selection requires genomic control of inbreeding

Background: In the past, pedigree relationships were used to control and monitor inbreeding because genomic relationships among selection candidates were not available until recently. The aim of this study was to understand the consequences for genetic variability across the genome when genomic information is used to estimate breeding values and in managing the inbreeding generated in the course of selection on genome-enhanced estimated breeding values. Methods: These consequences were measured by genetic gain, pedigreeand genome-based rates of inbreeding, and local inbreeding across the genome. Breeding schemes were compared by simulating truncation selection or optimum contribution selection with a restriction on pedigreeor genome-based inbreeding, and with selection using estimated breeding values based on genomeor pedigree-based BLUP. Trait information was recorded on full-sibs of the candidates. Results: When the information used to estimate breeding values and to constrain rates of inbreeding were either both pedigree-based or both genome-based, rates of genomic inbreeding were close to the desired values and the identical-by-descent profiles were reasonably uniform across the genome. However, with a pedigree-based inbreeding constraint and genome-based estimated breeding values, genomic rates of inbreeding were much higher than expected. With pedigree-instead of genome-based estimated breeding values, the impact of the largest QTL on the breeding values was much smaller, resulting in a more uniform genome-wide identical-by-descent profile but genomic rates of inbreeding were still higher than expected based on pedigree relationships, because they measure the inbreeding at a neutral locus not linked to any QTL. Neutral loci did not exist here, where there were 100 QTL on each chromosome. With a pedigree-based inbreeding constraint and genome-based estimated breeding values, genomic rates of inbreeding substantially exceeded the value of its constraint. In contrast, with a genome-based inbreeding constraint and genome-based estimated breeding values, marker frequencies changed, but this change was limited by the inbreeding constraint at the marker position. Conclusions: To control inbreeding, it is necessary to account for it on the same basis as what is used to estimate breeding values, i.e. pedigree-based inbreeding control with traditional pedigree-based BLUP estimated breeding values and genome-based inbreeding control with genome-based estimated breeding values. Background Traditional pedigree-based BLUP (Best Linear Unbiased Prediction) estimated breeding values (EBV) [1] are based on pedigree information and recordings of selection candidates and relatives, e.g. sibs of candidates, as in aquaculture breeding schemes, where many traits (e.g. disease resistance and fillet quality) cannot be measured on the candidates. For genomic breeding values, the effects of dense genetic markers are first estimated in a test population and later used to predict breeding values of selection candidates [2]. Genome-based EBV, i.e. EBV based on high-density marker data across the genome, generally have higher accuracy than pedigree-based BLUP EBV, because genetic markers provide a more accurate relationship matrix than pedigree [3], which is based on expected genetic relationships. For example, the expected relationship between two full-sibs is 0.5 but markers show that the true relationship deviates from 0.5 [4] and varies among pairs of sibs, depending on the * Correspondence: anna.sonesson@nofima.no Nofima AS, 1431 Ås, Norway Full list of author information is available at the end of the article Ge n e t i c s Se lec t ion Evolut ion © 2012 Sonesson et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Sonesson et al. Genetics Selection Evolution 2012, 44:27 http://www.gsejournal. org/content/44/1/27