F. V. Brito

Accuracy of genomic selection in simulated populations mimicking the extent of linkage disequilibrium in beef cattle

BackgroundThe success of genomic selection depends mainly on the extent of linkage disequilibrium (LD) between markers and quantitative trait loci (QTL), the number of animals in the training set (TS) and the heritability (h2) of the trait. The extent of LD depends on the genetic structure of the population and the density of markers. The aim of this study was to calculate accuracy of direct genomic estimated breeding values (DGEBV) using best linear unbiased genomic prediction (GBLUP) for different marker densities, heritabilities and sizes of the TS in simulated populations that mimicked previously reported extent and pattern of LD in beef cattle.ResultsThe accuracy of DGEBV increased significantly (p < 0.05) with the increase in the number of bulls in the TS (480, 960 or 1920), trait h2 (0.10, 0.25 or 0.40) and marker densities (40 k or 800 k). Increasing the number of animals in the TS by 4-fold and using their phenotypes to estimate marker effects was not sufficient to maintain or increase the accuracy of DGEBV obtained using estimated breeding values (EBVs) when the trait h2 was lower than 0.40 for both marker densities. Comparing to expected accuracies of parent average (PA), the gains by using DGEBV would be of 27%, 13% and 10% for trait h2 equal to 0.10, 0.25 and 0.40, respectively, considering the scenario with 40 k markers and 1920 bulls in TS.ConclusionsAs reported in dairy cattle, the size of the TS and the extent of LD have major impact on the accuracy of DGEBV. Based on the findings of this simulation study, large TS, as well as dense marker panels, aiming to increase the level of LD between markers and QTL, will likely be needed in beef cattle for successful implementation of genomic selection.

In-depth pedigree analysis in a large Brazilian Nellore herd

A large herd of Nellore cattle was evaluated using in-depth pedigree analyses. Taking into account the incomplete pedigree due to the use of multiple young sires for mating, the average inbreeding coefficient was calculated as 1.73% for the last generation, which was higher than the regular inbreeding coefficient (0.25%). The effective population size was estimated to be 114, 245, and 101 for the time periods 1995-1999, 1999-2003, and 2003-2007, respectively. Parameters based on the probability of gene origin were used to describe the genetic diversity over time in the herd. The effective number of founders, ancestors, and founder genomes decreased over time, showing an overall loss of genetic diversity. In the last five-year period (2003-2007), based on available pedigree information, one prominent ancestor contributed 10.6% to the gene pool of the herd, and 30% of this pool was contributed by 31 ancestors. The analysis of inbreeding under random mating indicated that the mating strategies used in the herd are slowing down inbreeding rates. However, it is advisable to continue monitoring the inbreeding rates and genetic diversity in this herd in the future.

Origins and genetic diversity of British cattle breeds in Brazil assessed by pedigree analyses

Pedigree information available for Angus (ANG), Devon (DEV), Hereford (HER), and Shorthorn (SHO) cattle in Brazil was analyzed to appraise the genetic diversity and population structure of these breeds. Pedigree records collected from the beginning of the 20th century until 2010 were used in the analyses. Over time, the number of herdbook registrations declined in HER after a peak in the 1970s, remained low in DEV and SHO, and increased steadily in ANG since the 1990s, such that it the latter is now the leading British cattle breed in Brazil. The average number of offspring registered per sire ranged from about 12 (SHO) to 20 (DEV) and the mean generation interval ranged from about 6.0 (HER and SHO) to 6.4 (ANG) years. In the reference population (calves born in 2009 and 2010, plus those born in 2008 for SHO) the mean equivalent number of generations known ranged from about 7 (SHO) to 9 (HER). In the 4 breeds studied, nearly all animals born over the last few years are inbred, even though the mean level of inbreeding in the reference population is below 4% in all breeds. The rate of inbreeding per generation, computed from the individual increase in inbreeding, ranged from about 0.2 (ANG) to 0.5% (DEV), with a corresponding effective population size of 245 and 92, respectively, which is above the recommended minimum critical threshold. The number of founders/ancestors contributing with 50% of the reference population gene pool was 211/26 for ANG, 41/14 for DEV, 164/25 for HER, and 79/10 for SHO, with effective number of founders/ancestors/founder genomes of 470/68/36, 89/33/16, 289/59/30, and 200/28/18 for ANG, DEV, HER, and SHO, respectively. The genetic contribution of different countries to the gene pool of each breed indicated that, throughout the period studied, DEV genes originated predominantly from the United Kingdom, while for the other breeds there was a changing pattern over time. Until the 1970s Argentina was the major supplier of ANG, while HER and SHO genes were mostly from Uruguay, but since then the United States took the leading role as supplier of ANG, HER, and SHO genes to Brazil. Our results reveal a mild increase in inbreeding in all breeds studied, with effective population size estimates indicating that reasonable levels of genetic diversity have been maintained in all 4 breeds. Continuous monitoring of inbreeding trends and of parameters derived from probability of gene origin should be ensured to warrant the long-term maintenance of genetic diversity.

A comprehensive comparison between single- and two-step GBLUP methods in a simulated beef cattle population

Abstract: The statistical methods used in the genetic evaluations are a key component of the process and can be best compared by using simulated data. The latter is especially true in grazing beef cattle production systems, where the number of proven bulls with highly reliable estimated breeding values is limited to allow for a trustworthy validation of genomic predictions. Therefore, we simulated data for 4980 beef cattle aiming to compare single-step genomic best linear unbiased prediction (ssGBLUP), which simultaneously incorporates pedigree, phenotypic, and genomic data into genomic evaluations, and two-step GBLUP (tsGBLUP) procedures and genomic estimated breeding values (GEBVs) blending methods. The greatest increases in GEBV accuracies compared with the parents’ average estimated breeding values (EBVPA) were 0.364 and 0.341 for ssGBLUP and tsGBLUP, respectively. Direct genomic value and GEBV accuracies when using ssGBLUP and tsGBLUP procedures were similar, except for the GEBV accuracies using Hayes’ blending method in tsGBLUP. There was no significant or slight bias in genomic predictions from ssGBLUP or tsGBLUP (using VanRaden’s blending method), indicating that these predictions are on the same scale compared with the true breeding values. Overall, genetic evaluations including genomic information resulted in gains in accuracy >100% compared with the EBVPA. In addition, there were no significant differences between the selected animals (10% males and 50% females) by using ssGBLUP or tsGBLUP.