Maysa Machado

Milk yield persistency in Brazilian Gyr cattle based on a random regression model

With the objective of evaluating measures of milk yield persistency, 27,000 test-day milk yield records from 3362 first lactations of Brazilian Gyr cows that calved between 1990 and 2007 were analyzed with a random regression model. Random, additive genetic and permanent environmental effects were modeled using Legendre polynomials of order 4 and 5, respectively. Residual variance was modeled using five classes. The average lactation curve was modeled using a fourth-order Legendre polynomial. Heritability estimates for measures of persistency ranged from 0.10 to 0.25. Genetic correlations between measures of persistency and 305-day milk yield (Y305) ranged from -0.52 to 0.03. At high selection intensities for persistency measures and Y305, few animals were selected in common. As the selection intensity for the two traits decreased, a higher percentage of animals were selected in common. The average predicted breeding values for Y305 according to year of birth of the cows had a substantial annual genetic gain. In contrast, no improvement in the average persistency breeding value was observed. We conclude that selection for total milk yield during lactation does not identify bulls or cows that are genetically superior in terms of milk yield persistency. A measure of persistency represented by the sum of deviations of estimated breeding value for days 31 to 280 in relation to estimated breeding value for day 30 should be preferred in genetic evaluations of this trait in the Gyr breed, since this measure showed a medium heritability and a genetic correlation with 305-day milk yield close to zero. In addition, this measure is more adequate at the time of peak lactation, which occurs between days 25 and 30 after calving in this breed.

Quantitative trait loci affecting milk production traits on bovine chromosome 6 in zebuine Gyr breed.

Fourteen Brazilian dairy Gyr sire families with 657 daughters were analyzed for quantitative trait loci (QTL) on chromosome 6 by using a daughter design for 5 economic traits: milk, fat, and protein production, fat and protein percentage. The cows and sires were genotyped for 27 microsatellites with average spacing between markers of 4.9 cM. In the analyses across 14 families, for the largest significant families, and within family, a QTL was located for milk yield and fat yield close to marker BMS2508 at the 5% chromosome-wide significance level across families and 1% chromosome-wide within families. For fat percentage, a QTL near DIK4482 was identified at the 5% chromosome-wide significance level when all families were analyzed together and at the 1% chromosome-wide significance level within the largest significant families. The different analyses yielded results that were generally consistent for milk yield, fat yield, and fat percentage. The order of the markers in the derived map was consistent with that in the consensus map. Some QTL and candidate genes in dairy cattle for milk production traits are probably preserved in Bos taurus and Bos indicus.

Technical note: A new and cost-effective method for detection of the bovine acyl-CoA:diacylglycerol acyltransferase 1 K232A polymorphism in cattle

A new, quick, and inexpensive method for detecting the bovine acyl-CoA:diacylglycerol acyltransferase1 (DGAT1) polymorphism (K232A) through tetra-primer amplification refractory mutation system by PCR (ARMS-PCR) was developed in the present investigation. The DGAT1 gene was recently identified as underlying variation in milk production traits. To date, PCR techniques such as PCR-RFLP have been used for detecting the DGAT1 K232A polymorphism, despite being expensive and laborious. The method proposed here, a tetra-primer ARMS-PCR, showed 100% sensitivity and specificity when compared with PCR-RFLP results obtained in a sample of 80 animals tested in a double-blind system. Our results suggest that the use of tetra-primer ARMS-PCR for DGAT1 K232A polymorphism genotyping could greatly reduce costs providing information for both research purposes and for dairy cattle breeders who perform DGAT1 genotyping for gene-assisted selection.

A new tetra-primer ARMS-PCR for genotyping bovine kappa-casein polymorphisms

Kappa-casein (κ-casein) is one of the most abundant milk proteins. Its main function is to avoid the aggregation of casein micelles, keeping them, and therefore calcium phosphate, in pockets in solution. In bovines, a κ-casein functional polymorphism has been associated with fat, calcium, and protein milk contents and faster curd contraction in cheese production. Quicker curd contraction reduces the loss of milk solids, enhancing cheese yield. This polymorphism induces a double amino acid substitution (Thr136Ile and Ala148Asp). The polymorphism is normally detected by PCR-RFLP, which is a laborious method. An interesting methodological alternative is the tetra-primer amplification refractory mutation system PCR (tetra-primer ARMS-PCR). A tetra-primer ARMS-PCR for the detection of this κ-casein polymorphism has been described. However, specificity was not achieved, probably due to problems with primer design. We developed a new tetra-primer ARMS-PCR for the detection of the κ-casein polymorphism. This new method was validated in a double-blind test, by comparison with the results obtained for 50 Guzerá bulls formerly genotyped by PCR-RFLP. This new method achieved 100% sensitivity and specificity. We conclude that this method is a useful, cost-efficient alternative for the detection of functional κ-casein polymorphisms.

Accuracy of genomic predictions in Gyr (Bos indicus) dairy cattle

Genomic selection may accelerate genetic progress in breeding programs of indicine breeds when compared with traditional selection methods. We present results of genomic predictions in Gyr (Bos indicus) dairy cattle of Brazil for milk yield (MY), fat yield (FY), protein yield (PY), and age at first calving using information from bulls and cows. Four different single nucleotide polymorphism (SNP) chips were studied. Additionally, the effect of the use of imputed data on genomic prediction accuracy was studied. A total of 474 bulls and 1,688 cows were genotyped with the Illumina BovineHD (HD; San Diego, CA) and BovineSNP50 (50K) chip, respectively. Genotypes of cows were imputed to HD using FImpute v2.2. After quality check of data, 496,606 markers remained. The HD markers present on the GeneSeek SGGP-20Ki (15,727; Lincoln, NE), 50K (22,152), and GeneSeek GGP-75Ki (65,018) were subset and used to assess the effect of lower SNP density on accuracy of prediction. Deregressed breeding values were used as pseudophenotypes for model training. Data were split into reference and validation to mimic a forward prediction scheme. The reference population consisted of animals whose birth year was ≤2004 and consisted of either only bulls (TR1) or a combination of bulls and dams (TR2), whereas the validation set consisted of younger bulls (born after 2004). Genomic BLUP was used to estimate genomic breeding values (GEBV) and reliability of GEBV (R2PEV) was based on the prediction error variance approach. Reliability of GEBV ranged from ∼0.46 (FY and PY) to 0.56 (MY) with TR1 and from 0.51 (PY) to 0.65 (MY) with TR2. When averaged across all traits, R2PEV were substantially higher (R2PEV of TR1 = 0.50 and TR2 = 0.57) compared with reliabilities of parent averages (0.35) computed from pedigree data and based on diagonals of the coefficient matrix (prediction error variance approach). Reliability was similar for all the 4 marker panels using either TR1 or TR2, except that imputed HD cow data set led to an inflation of reliability. Reliability of GEBV could be increased by enlarging the limited bull reference population with cow information. A reduced panel of ∼15K markers resulted in reliabilities similar to using HD markers. Reliability of GEBV could be increased by enlarging the limited bull reference population with cow information.

Red Sindhi cattle in Brazil: population structure and distribution

The Red Sindhi cattle breed was imported to Brazil in small numbers. Nowadays, the herds of this breed are distributed in the Northeast, Southeast and Midwest regions of the country. In this study, DNA samples of animals originating from 15 herds in the Northeast and Southeast regions have been analyzed to obtain the ancestry proportions, and to gain a better understanding of the current population structure of this breed in Brazil. Samples were genotyped using three different single nucleotide polymorphism (SNP) marker panels. Those markers have been used with the approach of unsupervised hierarchical clustering of individuals, and consequently, the ancestry of the population was divided into six different subpopulations. Three of those ancestry subpopulations were identified to be present in various different herds, while the other three were restricted to only one or two herds each. One of those herds has been kept isolated for more than 30 years, and it was identified to contain two almost exclusive subpopulations. To avoid important losses in the genetic diversity within the Red Sindhi breed in Brazil, we recommend the identification of superior sires from every subpopulation in the establishment of a breeding program for this breed.