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Using pooled data to estimate variance components and breeding values for traits affected by social interactions.

Peeters K, Ellen ED, Bijma P - Genet. Sel. Evol. (2013)

Bottom Line: In such cases, an individual's phenotype is affected by the direct (genetic) effect of the individual itself and the indirect (genetic) effects of the group mates.Using pooled data, the total genetic variance and breeding values can be estimated, but the underlying genetic components cannot.The most accurate estimates are obtained when group members belong to the same family.

View Article: PubMed Central - HTML - PubMed

Affiliation: Animal Breeding and Genomics Centre, Wageningen University, P,O, Box 338, 6700 AH Wageningen, The Netherlands. katrijn.peeters@wur.nl.

ABSTRACT

Background: Through social interactions, individuals affect one another's phenotype. In such cases, an individual's phenotype is affected by the direct (genetic) effect of the individual itself and the indirect (genetic) effects of the group mates. Using data on individual phenotypes, direct and indirect genetic (co)variances can be estimated. Together, they compose the total genetic variance that determines a population's potential to respond to selection. However, it can be difficult or expensive to obtain individual phenotypes. Phenotypes on traits such as egg production and feed intake are, therefore, often collected on group level. In this study, we investigated whether direct, indirect and total genetic variances, and breeding values can be estimated from pooled data (pooled by group). In addition, we determined the optimal group composition, i.e. the optimal number of families represented in a group to minimise the standard error of the estimates.

Methods: This study was performed in three steps. First, all research questions were answered by theoretical derivations. Second, a simulation study was conducted to investigate the estimation of variance components and optimal group composition. Third, individual and pooled survival records on 12 944 purebred laying hens were analysed to investigate the estimation of breeding values and response to selection.

Results: Through theoretical derivations and simulations, we showed that the total genetic variance can be estimated from pooled data, but the underlying direct and indirect genetic (co)variances cannot. Moreover, we showed that the most accurate estimates are obtained when group members belong to the same family. Additional theoretical derivations and data analyses on survival records showed that the total genetic variance and breeding values can be estimated from pooled data. Moreover, the correlation between the estimated total breeding values obtained from individual and pooled data was surprisingly close to one. This indicates that, for survival in purebred laying hens, loss in response to selection will be small when using pooled instead of individual data.

Conclusions: Using pooled data, the total genetic variance and breeding values can be estimated, but the underlying genetic components cannot. The most accurate estimates are obtained when group members belong to the same family.

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A^T’s obtained from individual data plotted against’s obtained from individual data and’s obtained from pooled data on survival in laying hens. A and B for data on W1 hens. C and D for data on WB hens. ∆G1 represents the total response to selection when selecting animals based on their  obtained from individual data or  obtained from pooled data. ∆G2 represents the total response to selection when selecting animals based on their  obtained from individual data.
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Figure 1: A^T’s obtained from individual data plotted against’s obtained from individual data and’s obtained from pooled data on survival in laying hens. A and B for data on W1 hens. C and D for data on WB hens. ∆G1 represents the total response to selection when selecting animals based on their obtained from individual data or obtained from pooled data. ∆G2 represents the total response to selection when selecting animals based on their obtained from individual data.

Mentions: To gain more insight, we calculated the loss in response to selection that occurs when applying a traditional model to individual or pooled data instead of a direct–indirect model to individual data. When applying a traditional model to individual data, the loss in total response to selection was 46.9% for W1 (Figure 1A) and 54.9% for WB (Figure 1C). When applying a traditional model to pooled data, the loss in total response to selection was 3.3% for W1 (Figure 1B) and 0.3% for WB (Figure 1D). In conclusion, the loss in total response to selection will be large when using a traditional animal model on individual data, but will be small when using a traditional animal model on pooled data. However, this outcome may be specific to this dataset. Survival in purebred laying hens was recorded in cages with four unrelated birds. Both direct and indirect genetic effects strongly influenced the trait. Group size, group composition, and the relative impact of direct and indirect genetic effects might influence the loss in total response to selection. For example, for body weight at 19 and 27 weeks of age, indirect genetic effects are expected to be small. In that case, an animal’s AT is mainly expressed in the phenotype of the animal itself. Consequently, we expect that more accurate estimated breeding values can be obtained when using individual instead of pooled data. Biscarini et al. [17] found a correlation of ~ 0.75 between the estimated breeding values based on individual and pooled data, resulting in a large loss in response to selection when using pooled instead of individual data. Thus, using pooled data does not always seem to be a proper alternative and requires further research.


Using pooled data to estimate variance components and breeding values for traits affected by social interactions.

Peeters K, Ellen ED, Bijma P - Genet. Sel. Evol. (2013)

A^T’s obtained from individual data plotted against’s obtained from individual data and’s obtained from pooled data on survival in laying hens. A and B for data on W1 hens. C and D for data on WB hens. ∆G1 represents the total response to selection when selecting animals based on their  obtained from individual data or  obtained from pooled data. ∆G2 represents the total response to selection when selecting animals based on their  obtained from individual data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3818455&req=5

Figure 1: A^T’s obtained from individual data plotted against’s obtained from individual data and’s obtained from pooled data on survival in laying hens. A and B for data on W1 hens. C and D for data on WB hens. ∆G1 represents the total response to selection when selecting animals based on their obtained from individual data or obtained from pooled data. ∆G2 represents the total response to selection when selecting animals based on their obtained from individual data.
Mentions: To gain more insight, we calculated the loss in response to selection that occurs when applying a traditional model to individual or pooled data instead of a direct–indirect model to individual data. When applying a traditional model to individual data, the loss in total response to selection was 46.9% for W1 (Figure 1A) and 54.9% for WB (Figure 1C). When applying a traditional model to pooled data, the loss in total response to selection was 3.3% for W1 (Figure 1B) and 0.3% for WB (Figure 1D). In conclusion, the loss in total response to selection will be large when using a traditional animal model on individual data, but will be small when using a traditional animal model on pooled data. However, this outcome may be specific to this dataset. Survival in purebred laying hens was recorded in cages with four unrelated birds. Both direct and indirect genetic effects strongly influenced the trait. Group size, group composition, and the relative impact of direct and indirect genetic effects might influence the loss in total response to selection. For example, for body weight at 19 and 27 weeks of age, indirect genetic effects are expected to be small. In that case, an animal’s AT is mainly expressed in the phenotype of the animal itself. Consequently, we expect that more accurate estimated breeding values can be obtained when using individual instead of pooled data. Biscarini et al. [17] found a correlation of ~ 0.75 between the estimated breeding values based on individual and pooled data, resulting in a large loss in response to selection when using pooled instead of individual data. Thus, using pooled data does not always seem to be a proper alternative and requires further research.

Bottom Line: In such cases, an individual's phenotype is affected by the direct (genetic) effect of the individual itself and the indirect (genetic) effects of the group mates.Using pooled data, the total genetic variance and breeding values can be estimated, but the underlying genetic components cannot.The most accurate estimates are obtained when group members belong to the same family.

View Article: PubMed Central - HTML - PubMed

Affiliation: Animal Breeding and Genomics Centre, Wageningen University, P,O, Box 338, 6700 AH Wageningen, The Netherlands. katrijn.peeters@wur.nl.

ABSTRACT

Background: Through social interactions, individuals affect one another's phenotype. In such cases, an individual's phenotype is affected by the direct (genetic) effect of the individual itself and the indirect (genetic) effects of the group mates. Using data on individual phenotypes, direct and indirect genetic (co)variances can be estimated. Together, they compose the total genetic variance that determines a population's potential to respond to selection. However, it can be difficult or expensive to obtain individual phenotypes. Phenotypes on traits such as egg production and feed intake are, therefore, often collected on group level. In this study, we investigated whether direct, indirect and total genetic variances, and breeding values can be estimated from pooled data (pooled by group). In addition, we determined the optimal group composition, i.e. the optimal number of families represented in a group to minimise the standard error of the estimates.

Methods: This study was performed in three steps. First, all research questions were answered by theoretical derivations. Second, a simulation study was conducted to investigate the estimation of variance components and optimal group composition. Third, individual and pooled survival records on 12 944 purebred laying hens were analysed to investigate the estimation of breeding values and response to selection.

Results: Through theoretical derivations and simulations, we showed that the total genetic variance can be estimated from pooled data, but the underlying direct and indirect genetic (co)variances cannot. Moreover, we showed that the most accurate estimates are obtained when group members belong to the same family. Additional theoretical derivations and data analyses on survival records showed that the total genetic variance and breeding values can be estimated from pooled data. Moreover, the correlation between the estimated total breeding values obtained from individual and pooled data was surprisingly close to one. This indicates that, for survival in purebred laying hens, loss in response to selection will be small when using pooled instead of individual data.

Conclusions: Using pooled data, the total genetic variance and breeding values can be estimated, but the underlying genetic components cannot. The most accurate estimates are obtained when group members belong to the same family.

Show MeSH