Corrigendum to: Genetic evaluation including intermediate omics features

Abstract The objective of this study was to evaluate the effects of various data structures on the genetic evaluation for the binary phenotype of reproductive success. The data were simulated based on an existing pedigree and an underlying fertility phenotype with a heritability of 0.10. A data set of complete observations was generated for all cows. This data set was then modified mimicking the culling of cows when they first failed to reproduce, cows having a missing observation at either their second or fifth opportunity to reproduce as if they had been selected as donors for embryo transfer, and censoring records following the sixth opportunity to reproduce as in a cull-for-age strategy. The data were analyzed using a third order polynomial random regression model. The EBV of interest for each animal was the sum of the age-specific EBV over the first 10 observations (reproductive success at ages 2-11). Thus, the EBV might be interpreted as the genetic expectation of number of calves produced when a female is given ten opportunities to calve. Culling open cows resulted in the EBV for 3 year-old cows being reduced from 8.27 ± 0.03 when open cows were retained to 7.60 ± 0.02 when they were culled. The magnitude of this effect decreased as cows grew older when they first failed to reproduce and were subsequently culled. Cows that did not fail over the 11 years of simulated data had an EBV of 9.43 ± 0.01 and 9.35 ± 0.01 based on analyses of the complete data and the data in which cows that failed to reproduce were culled, respectively. Cows that had a missing observation for their second record had a significantly reduced EBV, but the corresponding effect at the fifth record was negligible. The current study illustrates that culling and management decisions, and particularly those that impact the beginning of the trajectory of sustained reproductive success, can influence both the magnitude and accuracy of resulting EBV.

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Genetic evaluation is possible on community pastoral small ruminant flocks in the presence of multiple sires and uncertain of paternity

Small Ruminant Research ◽

10.1016/j.smallrumres.2017.04.017 ◽

2017 ◽

Vol 151 ◽

pp. 72-81

Author(s):

Francisco Flávio Dias Carneiro ◽

Ana Maria Bezerra Oliveira Lôbo ◽

Luciano Pinheiro da Silva ◽

Kleibe de Moraes Silva ◽

Aline Vieira Landim ◽

...

Keyword(s):

Small Ruminant ◽

Genetic Evaluation

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GPS Coordinates for Modelling Correlated Herd Effects in Genomic Prediction Models Applied to Hanwoo Beef Cattle

Animals ◽

10.3390/ani11072050 ◽

2021 ◽

Vol 11 (7) ◽

pp. 2050

Author(s):

Beatriz Castro Dias Cuyabano ◽

Gabriel Rovere ◽

Dajeong Lim ◽

Tae Hun Kim ◽

Hak Kyo Lee ◽

...

Keyword(s):

Environmental Factors ◽

Genomic Prediction ◽

Prediction Models ◽

Phenotypic Expression ◽

Genetic Evaluation ◽

Genomic Breeding ◽

Breeding Values ◽

Korean Cattle ◽

Evaluation Programs ◽

The Impact

It is widely known that the environment influences phenotypic expression and that its effects must be accounted for in genetic evaluation programs. The most used method to account for environmental effects is to add herd and contemporary group to the model. Although generally informative, the herd effect treats different farms as independent units. However, if two farms are located physically close to each other, they potentially share correlated environmental factors. We introduce a method to model herd effects that uses the physical distances between farms based on the Global Positioning System (GPS) coordinates as a proxy for the correlation matrix of these effects that aims to account for similarities and differences between farms due to environmental factors. A population of Hanwoo Korean cattle was used to evaluate the impact of modelling herd effects as correlated, in comparison to assuming the farms as completely independent units, on the variance components and genomic prediction. The main result was an increase in the reliabilities of the predicted genomic breeding values compared to reliabilities obtained with traditional models (across four traits evaluated, reliabilities of prediction presented increases that ranged from 0.05 ± 0.01 to 0.33 ± 0.03), suggesting that these models may overestimate heritabilities. Although little to no significant gain was obtained in phenotypic prediction, the increased reliability of the predicted genomic breeding values is of practical relevance for genetic evaluation programs.

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