Trends of the genetic connectedness measures among Nelore beef cattle herds

An assessment of genomic connectedness measures in Nellore cattle

An important criterion to consider in genetic evaluations is the extent of genetic connectedness across management units (MU), especially if they differ in their genetic mean. Reliable comparisons of genetic values across MU depend on the degree of connectedness: the higher the connectedness, the more reliable the comparison. Traditionally, genetic connectedness was calculated through pedigree-based methods; however, in the era of genomic selection, this can be better estimated utilizing new approaches based on genomics. Most procedures consider only additive genetic effects, which may not accurately reflect the underlying gene action of the evaluated trait, and little is known about the impact of non-additive gene action on connectedness measures. The objective of this study was to investigate the extent of genomic connectedness measures, for the first time, in Brazilian field data by applying additive and non-additive relationship matrices using a fatty acid profile data set from seven farms located in the three regions of Brazil, which are part of the three breeding programs. Myristic acid (C14:0) was used due to its importance for human health and reported presence of non-additive gene action. The pedigree included 427,740 animals and 925 of them were genotyped using the Bovine high-density genotyping chip. Six relationship matrices were constructed, parametrically and non-parametrically capturing additive and non-additive genetic effects from both pedigree and genomic data. We assessed genome-based connectedness across MU using the prediction error variance of difference (PEVD) and the coefficient of determination (CD). PEVD values ranged from 0.540 to 1.707, and CD from 0.146 to 0.456. Genomic information consistently enhanced the measures of connectedness compared to the numerator relationship matrix by at least 63%. Combining additive and non-additive genomic kernel relationship matrices or a non-parametric relationship matrix increased the capture of connectedness. Overall, the Gaussian kernel yielded the largest measure of connectedness. Our findings showed that connectedness metrics can be extended to incorporate genomic information and non-additive genetic variation using field data. We propose that different genomic relationship matrices can be designed to capture additive and non-additive genetic effects, increase the measures of connectedness, and to more accurately estimate the true state of connectedness in herds.

Population structure and genetic diversity in Colombian Simmental cattle

A vital requirement to design and implement a breeding program is to know the structure and genetic diversity of a population. The aim of this study was to characterize population structure and genetic diversity of the Colombian Simmental cattle. The pedigree file included 27,985 animals born from 1975 to 2017. The level of genetic diversity and breed structure was evaluated through probabilities of gene origin expressed via effective number of founders, ancestors and founders genomes. The inbreeding rates and the degree of genetic connectivity were estimated using a regression analysis and a genetic drift variance analysis, respectively. The lowest effective number of founders and ancestors were 50 and 38 by year, respectively. The average inbreeding by year of birth decreased from 5.06% in 1980 to 2.25% in 2017. The dairy line genetic contributions in the overall population increased significantly in the last 37 years, and the beef line contribution decreased. Regarding the genetic connectivity, Colombian regions (administrative divisions) with the largest cattle population had higher values. The results indicate that the availability of European and North American bulls contributes to genetic diversity by increasing the effective number of founders over time in the Colombian Simmental cattle population. However, the intensive use of relatively few founders causes an unbalanced genetic contribution and the loss of genetic diversity by gene pool erosion.