RESUMEN
The aim of this study was to evaluate the effect of including calving age (CA) on genetic evaluation models for Holstein cattle. The evaluated models included the permanent environment, the sire-herd interactions, and the animals and residual effects as random. The fixed effects included the average production of milk, fat, and protein and the herd-year-season effect. The analyzed data included 603,521 records of milk production (in kg) corresponding to 438,098 animals from 527 herds. Additionally, there were 179,122 records of fat and protein components, corresponding to 148,930 animals from 137 herds. The records were classified by first lactation only (FL) and all available lactations (AL) for validation test (VT). The FL records corresponded to 275,487 milk production records with a mean of 10,874.1 ± 2773.9 kg at a mean CA of 25.6 ± 4.2 months. For FL, the milk components consisted of 78,955 records with a mean fat production of 392.86 ± 89.9 kg, a mean protein production of 362.8 ± 74.9 kg and a mean CA of 25.2 ± 4.1 months. For AL, the number of records was 603,521 for milk production with a mean of 10,802.8 ± 2905.9 kg and a mean CA of 35.6 ± 11.5 months. For the milk components, there were 179,122 records with a mean of 36.1 ± 9.5 months for CA and 388.3 ± 98.4 kg and 356.7 ± 82.6 kg for fat and protein, respectively. Three models were compared: the base model (M0) described above, and two alternative models that included CA in a linear and quadratic form (M1 and M2, respectively). Estimations of the variance components (VC) and breeding value (BV) were obtained using a repeatability animal model, with the same phenotypic and pedigree information used for all models. To select the best fit model for the data, a likelihood ratio test (LRtest) was used. A validation test (VT) was also applied to each model to evaluate the consistency of the genetic trends for females with information on AL and FL. The inclusion of CA in its linear form (M1) was the model that achieved the best results in the LRtest and an acceptable value for the VT. These results show that CA improves the model fit for BV prediction and reliability.
Asunto(s)
Bovinos/fisiología , Lactancia/genética , Leche/metabolismo , Reproducción , Factores de Edad , Animales , Bovinos/genética , Femenino , México , Modelos Teóricos , Reproducibilidad de los ResultadosRESUMEN
Genomic selection has been proposed for the mitigation of methane (CH4) emissions by cattle because there is considerable variability in CH4 emissions between individuals fed on the same diet. The genome-wide association study (GWAS) represents an important tool for the detection of candidate genes, haplotypes or single nucleotide polymorphisms (SNP) markers related to characteristics of economic interest. The present study included information for 280 cows in three dairy production systems in Mexico: 1) Dual Purpose (n = 100), 2) Specialized Tropical Dairy (n = 76), 3) Familiar Production System (n = 104). Concentrations of CH4 in a breath of individual cows at the time of milking (MEIm) were estimated through a system of infrared sensors. After quality control analyses, 21,958 SNPs were included. Associations of markers were made using a linear regression model, corrected with principal component analyses. In total, 46 SNPs were identified as significant for CH4 production. Several SNPs associated with CH4 production were found at regions previously described for quantitative trait loci of composition characteristics of meat, milk fatty acids and characteristics related to feed intake. It was concluded that the SNPs identified could be used in genomic selection programs in developing countries and combined with other datasets for global selection.
Asunto(s)
Alimentación Animal , Bovinos/genética , Metano/metabolismo , Modelos Genéticos , Polimorfismo de Nucleótido Simple , Animales , Marcadores Genéticos , Estudio de Asociación del Genoma CompletoRESUMEN
Genetic variation enables both adaptive evolutionary changes and artificial selection. Genetic makeup of populations is the result of a long-term process of selection and adaptation to specific environments and ecosystems. The aim of this study was to characterize the genetic variability of México's chicken population to reveal any underlying population structure. A total of 213 chickens were sampled in different rural production units located in 25 states of México. Genotypes were obtained using the Affymetrix Axiom® 600 K Chicken Genotyping Array. The Identity by Descent (IBD) and the principal components analysis (PCA) were performed by SVS software on pruned single nucleotide polymorphisms (SNPs).ADMIXTURE analyses identified 3 ancestors and the proportion of the genetic contribution of each of them has been determined in each individual. The results of the Neighbor-Joining (NJ) analysis resulted consistent with those obtained by the PCA. All methods utilized in this study did not allow a classification of Mexican chicken in distinct clusters or groups. A total of 3,059 run of homozygosity (ROH) were identified and, being mainly short in length (<4 Mb), these regions are indicative of a low inbreeding level in the population. Finally, findings from the ROH analysis indicated the presence of natural selective pressure in the population of Mexican chicken.The study indicates that the Mexican chicken clearly appear to be a unique creole chicken population that was not subjected to a specific artificial selection. Results provide a genetic knowledge that can be used as a basis for the genetic management of a unique and very large creole population, especially in the view of using it in production of hybrids to increase the productivity and economic revenue of family farming agriculture, which is widely present in México.
Asunto(s)
Pollos/genética , Variación Genética , Selección Genética , Animales , Marcadores Genéticos , México , Polimorfismo de Nucleótido Simple , Análisis de Componente PrincipalRESUMEN
BACKGROUND: Copy number variations are genome polymorphism that influence phenotypic variation and are an important source of genetic variation in populations. The aim of this study was to investigate genetic variability in the Mexican Creole chicken population using CNVs. RESULTS: The Hidden Markov Model of the PennCNV software detected a total of 1924 CNVs in the genome of the 256 samples processed with Axiom® Genome-Wide Chicken Genotyping Array (Affymetrix). The mapped CNVs comprised 1538 gains and 386 losses, resulting at population level in 1216 CNV regions (CNVRs), of which 959 gains, 226 losses and 31 complex (i.e. containing both losses and gains). The CNVRs covered a total of 47 Mb of the whole genome sequence length, corresponding to 5.12% of the chicken galGal4 autosome assembly. CONCLUSIONS: This study allowed a deep insight into the structural variation in the genome of unselected Mexican chicken population, which up to now has not been genetically characterized. The genomic study disclosed that the population, even if presenting extreme morphological variation, cannot be organized in differentiated genetic subpopulations. Finally this study provides a chicken CNV map based on the 600 K SNP chip array jointly with a genome-wide gene copy number estimates in a native unselected for more than 500 years chicken population.