scholarly journals Genetic evaluation of sheep for resistance to gastrointestinal nematodes and body size including genomic information

2020 ◽  
Author(s):  
Tatiana Saraiva Torres ◽  
Luciano Silva Sena ◽  
Gleyson Vieira dos Santos ◽  
Luiz Antonio Silva Figueiredo Filho ◽  
Bruna Lima Barbosa ◽  
...  
Keyword(s):  
Body Size ◽  
Gastrointestinal Nematodes ◽  
Genetic Evaluation ◽  
Genomic Information

scholarly journals Prediction of breeding values for group-recorded traits including genomic information and an individually recorded correlated trait

Heredity ◽  
2020 ◽  
Vol 126 (1) ◽  
pp. 206-217
Author(s):  
Xiang Ma ◽  
Ole F. Christensen ◽  
Hongding Gao ◽  
Ruihua Huang ◽  
Bjarne Nielsen ◽  
...  
Keyword(s):  
Egg Production ◽  
Quantitative Traits ◽  
Group Structure ◽  
Genetic Evaluation ◽  
Genomic Information ◽  
Bivariate Model ◽  
Breeding Values ◽  
Lower Accuracy ◽  
Small Group Size ◽  
Estimated Breeding Values

AbstractRecords on groups of individuals could be valuable for predicting breeding values when a trait is difficult or costly to measure on single individuals, such as feed intake and egg production. Adding genomic information has shown improvement in the accuracy of genetic evaluation of quantitative traits with individual records. Here, we investigated the value of genomic information for traits with group records. Besides, we investigated the improvement in accuracy of genetic evaluation for group-recorded traits when including information on a correlated trait with individual records. The study was based on a simulated pig population, including three scenarios of group structure and size. The results showed that both the genomic information and a correlated trait increased the accuracy of estimated breeding values (EBVs) for traits with group records. The accuracies of EBV obtained from group records with a size 24 were much lower than those with a size 12. Random assignment of animals to pens led to lower accuracy due to the weaker relationship between individuals within each group. It suggests that group records are valuable for genetic evaluation of a trait that is difficult to record on individuals, and the accuracy of genetic evaluation can be considerably increased using genomic information. Moreover, the genetic evaluation for a trait with group records can be greatly improved using a bivariate model, including correlated traits that are recorded individually. For efficient use of group records in genetic evaluation, relatively small group size and close relationships between individuals within one group are recommended.

scholarly journals Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score

2010 ◽  
Vol 93 (2) ◽  
pp. 743-752 ◽  
Author(s):  
I. Aguilar ◽  
I. Misztal ◽  
D.L. Johnson ◽  
A. Legarra ◽  
S. Tsuruta ◽  
...  
Keyword(s):  
Genetic Evaluation ◽  
Genomic Information ◽  
Unified Approach ◽  
Final Score

scholarly journals Genetic evaluation of tropical climate-adapted sheep for carcass traits including genomic information

2020 ◽  
Vol 188 ◽  
pp. 106120
Author(s):  
Luciano Silva Sena ◽  
Luiz Antonio Silva Figueiredo Filho ◽  
Gleyson Vieira dos Santos ◽  
Antônio de Sousa Júnior ◽  
Natanael Pereira da Silva Santos ◽  
...  
Keyword(s):  
Carcass Traits ◽  
Tropical Climate ◽  
Genetic Evaluation ◽  
Genomic Information

Developments in genetic evaluation: from test days to genomics

2005 ◽  
Vol 2005 ◽  
pp. 237-237
Author(s):  
T. H. E. Meuwissen
Keyword(s):  
Best Linear Unbiased Prediction ◽  
Genetic Evaluation ◽  
Genomic Information ◽  
Linear Unbiased Prediction ◽  
The Past ◽  
The Future ◽  
Best Linear Unbiased ◽  
Genetic Evaluations ◽  
Direct Use ◽  
Unbiased Prediction

Genetic evaluations have come a long way during the past decades, where the development and implementation of Best Linear Unbiased Prediction (BLUP) was undoubtedly the most notable achievement. The most important advances during the past 10 years were probably the direct use of test-day data in the BLUP model, ie. test-day models, the correction for heterogeneous within herd variances, multiple across country genetic evaluations (MACE), and the inclusion of more and more functional, and often difficult, traits in the evaluations. This paper will review the developments in test-day models, and the future of the genetic evaluations field, namely the inclusion of genomic information in the evaluations.

scholarly journals Genomic data enables genetic evaluation using data recorded on low-middle income country smallholder dairy farms

2019 ◽  
Author(s):  
Owen Powell ◽  
Raphael Mrode ◽  
R. Chris Gaynor ◽  
Martin Johnsson ◽  
Gregor Gorjanc ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Production Systems ◽  
Genomic Data ◽  
Genetic Evaluation ◽  
Pedigree Information ◽  
Dairy Production ◽  
Genomic Information ◽  
Smallholder Dairy ◽  
Genetic Evaluations ◽  
Smallholder Dairy Production

AbstractBackgroundGenetic evaluation is a central component of a breeding program. In advanced economies, most genetic evaluations depend on large quantities of data that are recorded on commercial farms. Large herd sizes and widespread use of artificial insemination create strong genetic connectedness that enables the genetic and environmental effects of an individual animal’s phenotype to be accurately separated. In contrast to this, herds are neither large nor have strong genetic connectedness in smallholder dairy production systems of many low to middle-income countries (LMIC). This limits genetic evaluation, and furthermore, the pedigree information needed for traditional genetic evaluation is typically unavailable. Genomic information keeps track of shared haplotypes rather than shared relatives. This information could capture and strengthen genetic connectedness between herds and through this may enable genetic evaluations for LMIC smallholder dairy farms. The objective of this study was to use simulation to quantify the power of genomic information to enable genetic evaluation under such conditions.ResultsThe results from this study show: (i) the genetic evaluation of phenotyped cows using genomic information had higher accuracy compared to pedigree information across all breeding designs; (ii) the genetic evaluation of phenotyped cows with genomic information and modelling herd as a random effect had higher or equal accuracy compared to modelling herd as a fixed effect; (iii) the genetic evaluation of phenotyped cows from breeding designs with strong genetic connectedness had higher accuracy compared to breeding designs with weaker genetic connectedness; (iv) genomic prediction of young bulls was possible using marker estimates from the genetic evaluations of their phenotyped dams. For example, the accuracy of genomic prediction of young bulls from an average herd size of 1 (μ=1.58) was 0.40 under a breeding design with 1,000 sires mated per generation and a training set of 8,000 phenotyped and genotyped cows.ConclusionsThis study demonstrates the potential of genomic information to be an enabling technology in LMIC smallholder dairy production systems by facilitating genetic evaluations with in-situ records collected from farms with herd sizes of four cows or less. Across a range of breeding designs, genomic data enabled accurate genetic evaluation of phenotyped cows and genomic prediction of young bulls using data sets that contained small herds with weak genetic connections. The use of smallholder dairy data in genetic evaluations would enable the establishment of breeding programs to improve in-situ germplasm and, if required, would enable the importation of the most suitable external germplasm. This could be individually tailored for each target environment. Together this would increase the productivity, profitability and sustainability of LMIC smallholder dairy production systems. However, data collection, including genomic data, is expensive and business models will need to be carefully constructed so that the costs are sustainably offset.

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