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

Abstract

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 (R) 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, R were substantially higher (R 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.

Integrating Genetic and Genomic Analyses of Combined Health Data Across Ecotypes to Improve Disease Resistance in Indigenous African Chickens

“…Since it uses all available data, forward validation might expedite genomic selection response. This approach has previously been successfully applied in studies of indigenous cattle breeds (Neves et al, 2014;Silva et al, 2016;Boison et al, 2017) but not of chicken.…”

Genomic Selection and Use of Molecular Tools in Breeding Programs for Indigenous and Crossbred Cattle in Developing Countries: Current Status and Future Prospects

“…In general, these reported studies on genomic prediction in dairy and beef cattle are characterized by small reference populations (500–3,000 animals, Table 1) and most validations are undertaken in test data sets created by either random or structured sampling from all genotyped animals. A few of these reference populations are a combination of both bulls and cows (Boison et al, 2017) but most are cows (Brown et al, 2016; Silva et al, 2016). This has implications in terms of the accuracy of genomic prediction, which has tended to be lower compared to those obtained in developed countries, given the limited information of the response variable when using cow records.…”

“…In developing countries especially in Africa and Asia, most of the production occurs in small holder systems which are characterized by small herd sizes, lack of performance, and pedigree recording and therefore, the non-existence of conventional genetic evaluation systems (Kosgey and Okeyo, 2007). However, in some countries like Brazil in Latin America, the existence of breed associations have resulted in the establishment of some degree of data and pedigree recording and genetic evaluation (Silva et al, 2016; Boison et al, 2017), but there is still the lack of breeding structures such as AI companies, to drive breed improvement programs. Therefore in the era of genomics, most genotyping activities in developing countries are undertaken by breed organizations or associations, such as in Brazil (Carvalheiro, 2014; Silva et al, 2016), or are a result of several development projects, such as the East Africa Dairy Development Project (Brown et al, 2016), and the African Dairy Genetic Gains Cattle project (https://www.ilri.org/node/40458).…”

Evaluation of single nucleotide polymorphisms identified through the use of SNP assay in Romanian and Chinese Holstein and Simmental cattle breeds

“…In bovine, genotyping using SNP array has become a common practice in developed countries, for both dairy and beef cattle breeding programs applying genomic selection. The assay was used in genetic disease mapping (Charlier et al, 2008;VanRaden et al, 2011;Murgiano et al 2014), genomic selection for economic traits (Neves et al, 2014;Garcia-Ruiz et al, 2016;Taylor et al, 2016;Brown et al, 2016;Boison et al, 2017) and genome-wide association studies (Bolorma et al, 2011;Olsen et al, 2011;Wu et al, 2013;Lee et al, 2013;Streit et al, 2013;Tiezzi et al, 2015). Since the bovine genome sequencing, several SNP arrays from Illumina, Affymetrix and Neogen/GeneSeek were developed and are currently available for cattle (Khatkar et al, 2010;Kasarda et al, 2014), such as lower-density SNP panels (3K, 7K, 15K, 25K), medium (50K) up to high-density SNP panel (150K, 250K, 650K, 800K).…”