Study on the introgression of beef breeds in Canchim cattle using single nucleotide polymorphism markers.
The aim of this study was to evaluate the level of introgression of breeds in the Canchim (CA: 62.5% Charolais-37.5% Zebu) and MA genetic group (MA: 65.6% Charolais-34.4% Zebu) cattle using genomic information on Charolais (CH), Nelore (NE), and Indubrasil (IB) breeds. The number of animals used was 395 (CA and MA), 763 (NE), 338 (CH), and 37 (IB). The Bovine50SNP BeadChip from Illumina panel was used to estimate the levels of introgression of breeds considering the Maximum likelihood, Bayesian, and Single Regression method. After genotype quality control, 32,308 SNPs were considered in the analysis. Furthermore, three thresholds to prune out SNPs in linkage disequilibrium higher than 0.10, 0.05, and 0.01 were considered, resulting in 15,286, 7,652, and 1,582 SNPs, respectively. For k = 2, the proportion of taurine and indicine varied from the expected proportion based on pedigree for all methods studied. For k = 3, the Regression method was able to differentiate the animals in three main clusters assigned to each purebred breed, showing more reasonable according to its biological viewpoint. Analyzing the data considering k = 2 seems to be more appropriate for Canchim-MA animals due to its biological interpretation. The usage of 32,308 SNPs in the analyses resulted in similar findings between the estimated and expected breed proportions. Using the Regression approach, a contribution of Indubrasil was observed in Canchim-MA when k = 3 was considered. Genetic parameter estimation could account for this breed composition information as a source of variation in order to improve the accuracy of genetic models. Our findings may help assemble appropriate reference populations for genomic prediction for Canchim-MA in order to improve prediction accuracy. Using the information on the level of introgression in each individual could also be useful in breeding or crossing design to improve individual heterosis in crossbred cattle.
Departamento de Ciências Exatas, Universidade Estadual Paulista (Unesp), Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, São Paulo, Brazil.
Marcos Eli
ME
Buzanskas
Beef Improvement Opportunities (BIO), Guelph, Ontario, Canada.|Departamento de Ciências Básicas, Universidade de São Paulo (USP), Faculdade de Zootecnia e Engenharia de Alimentos (FZEA), Pirassununga, São Paulo, Brazil.
Ricardo Vieira
RV
Ventura
Departamento de Ciências Exatas, Universidade Estadual Paulista (Unesp), Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, São Paulo, Brazil.
Tatiane Cristina
TC
Seleguim Chud
Departamento de Ciências Exatas, Universidade Estadual Paulista (Unesp), Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, São Paulo, Brazil.
Priscila Arrigucci
PA
Bernardes
Departamento de Zootecnia, Universidade Estadual Paulista (Unesp), Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, São Paulo, Brazil.
Daniel Jordan de Abreu
DJ
Santos
Embrapa Southeast Livestock, São Carlos, São Paulo, Brazil.
Luciana Correia de Almeida
LC
Regitano
Embrapa Southeast Livestock, São Carlos, São Paulo, Brazil.
Maurício Mello de
MM
Alencar
Embrapa Agricultural Informatics, Campinas, São Paulo, Brazil.
Maurício de Alvarenga
MA
Mudadu
Faculdade de Agronomia e Medicina Veterinária - Universidade de Passo Fundo, Passo Fundo, Rio Grande do Sul, Brazil.
Ricardo
R
Zanella
Embrapa Dairy Cattle, Juiz de Fora, Minas Gerais, Brazil.
Marcos Vinícius Gualberto Barbosa
MV
da Silva
Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada.|Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, 6000 C&E Trail, Lacombe, Alberta, Canada.
Changxi
C
Li
Department of Animal and Poultry Science, University of Guelph, Centre for Genetic Improvement of Livestock (CGIL), Guelph, Ontario, Canada.
Flavio Schramm
FS
Schenkel
Departamento de Ciências Exatas, Universidade Estadual Paulista (Unesp), Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, São Paulo, Brazil.
Danísio Prado
DP
Munari
D000818:Animals; D001823:Body Composition / Q000235:genetics*; D001947:Breeding / Q000379:methods; D002417:Cattle / Q000235:genetics*; D005260:Female; D056726:Genetic Association Studies; D006823:Hybrid Vigor / Q000235:genetics; D006824:Hybridization, Genetic / Q000235:genetics; D015810:Linkage Disequilibrium; D008297:Male; D020641:Polymorphism, Single Nucleotide; D019655:Quantitative Trait, Heritable; D000069466:Red Meat