Trends of the genetic connectedness measures among Nelore beef cattle herds.
Validity of comparisons between expected breeding values obtained from best linear unbiased prediction procedures in genetic evaluations is dependent on genetic connectedness among herds. Different cattle breeding programmes have their own particular features that distinguish their database structure and can affect connectedness. Thus, the evolution of these programmes can also alter the connectedness measures. This study analysed the evolution of the genetic connectedness measures among Brazilian Nelore cattle herds from 1999 to 2008, using the French Criterion of Admission to the group of Connected Herds (CACO) method, based on coefficients of determination (CD) of contrasts. Genetic connectedness levels were analysed by using simple and multiple regression analyses on herd descriptors to understand their relationship and their temporal trends from the 1999-2003 to the 2004-2008 period. The results showed a high level of genetic connectedness, with CACO estimates higher than 0.4 for the majority of them. Evaluation of the last 5-year period showed only a small increase in average CACO measures compared with the first 5 years, from 0.77 to 0.80. The percentage of herds with CACO estimates lower than 0.7 decreased from 27.5% in the first period to 16.2% in the last one. The connectedness measures were correlated with percentage of progeny from connecting sires, and the artificial insemination spread among Brazilian herds in recent years. But changes in connectedness levels were shown to be more complex, and their complete explanation cannot consider only herd descriptors. They involve more comprehensive changes in the relationship matrix, which can be only fully expressed by the CD of contrasts.
Departamento de Genética, Faculdade de Medicina de Ribeirão Preto, USP, Ribeirão Preto, SP, Brazil. newton.pegolo@gmail.com
N T
NT
Pegolo
D
D
Laloë
H N
HN
de Oliveira
R B
RB
Lôbo
M-N
MN
Fouilloux
D000818:Animals; D001947:Breeding; D002417:Cattle / Q000235:genetics*; D016208:Databases, Factual; D005260:Female; D016014:Linear Models; D010802:Phylogeny; D012044:Regression Analysis