Four different methods of implementing OCS in the Norwegian Cheviot population has been tested. They all have advantages and disadvantages, and some are more realistic to implement than others.
As seen in Table 15, the four different selection methods have some small differences between number of rams selected and percentage contributions.
The total number of rams selected varies from 70-92. One of the reasons why there is only 70 rams selected in scenario 4 with pre-selected elite and test rams and 90 in the actual selection, is because there are 19 rams with registered offspring in 2015 that did not meet the criteria to be included in the dataset. Four of them are the older AI rams, 10 are older elite rams and five are young rams born in 2014. The older AI rams are excluded from the dataset because the two rams that NSG selected for AI in 2014 died, and the older AI rams had frozen doses left over from previous season that were made available for sale in the 2014 mating season instead. The older elite rams had already been used at least one season as elite rams, and the dataset only included elite ram candidates born in 2013. One of the young rams not included in the dataset as selection candidate, but still had registered offspring in 2015 is a ram that were born in a flock that previously belonged to ram circle 141, but not in 2014. However, the ram was promising and the ram circle board included the ram as a test ram in the ram circle.
Why the other four young rams were not included in the dataset as test ram candidates is unknown.
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Table 15. Number of rams, percent contribution, average O-index, Average contribution per ram and average ewes mated per ram for each of the four scenarios and for the actual selection.
Selection Scenario
Scenario 1 Scenario 2 Scenario 3 Scenario 4 Actual Selection (scenario 0)
Number of AI rams 8 3 6 1 5(3)*
% contribution by AI rams 30.0 12.4 6.6 8.23 4.6
Number of Elite rams 18 19 23 15 26
% contribution by Elite rams
15.8 18.6 25.3 19.95 24.6
Number of Test rams 55 68 63 54 59
% contribution by Test rams
55.9 69.0 68.1 71.82 70.8
Total number of rams 81 90 92 70 90
Average O-Index for selected rams
125.9 125.2 125.7 122.6 122.7
Average contribution per ram
1.04 1.06 1.09 1.4 1.2
Average ewes mated per ram
18.2 18.5 19.1 25.0 19.5
*There were three selected rams for AI, but because two of them died, frozen semen from four older rams were made available and these are the AI rams with tagged offspring in 2015.
The within ram circle selection approach (scenario 1) would optimize the selection for one ram circle at a time and thus make each ram circle a separate population. This would make it possible to use OCS on all ram type selections and at the same time ensure an appropriate number of rams is selected for each ram circle. The average O-index for this selection
scenario is marginally higher than the across ram circle selection (scenario 3). Eight different AI rams were selected for the within-ram circle selection method, but not all of them were selected for all ram circles. Ram circle 141 had only four AI rams selected, and ram circle 171 had eight AI rams selected. If this method were to be applied, maybe one option could be to only put the four rams jointly selected by the four ram circles on AI station and let the rest of the rams suggested for AI be used as elite ram for another season in their respective ram circle.
31 The biggest issue with the within ram circle selection method (scenario 1) is the
unproportioned selection of AI rams. This is solved in selection scenario 2, where the AI rams are pre-selected. Within ram circle selection with pre-selected AI rams (scenario 2) is the only one of the suggested selection scenarios that would allow NSG to select the 2-4 AI rams needed for AI. Gencont 2 still optimizes their respective contribution. The optimization process will be done within ram circles for the test- and elite rams. The average O-index for the rams selected with selection scenario 2 is not significantly different from the average O-index for the rams selected without pre-selection (scenario 1). However, there is a significant difference between this selection scenario and the actual ram selection (scenario 0) (p<0.01).
The results for the across ram circle selection scenario (scenario 3) show that Gencont 2 selects the same total number of rams as the actual selection (scenario 0), but more rams from ram circle 141 compared to the other ram circles. The reason there is a difference between how many rams are selected from each ram circle is probably that there is a big difference in the genetic level of the four ram circles. In addition, the relatedness to the rest of the ram circles can influence the selection. Ram circle 141 has a low population average relationship and a high average breeding value compared to the other ram circles. Ram circle 141 is also the largest ram circle with more candidates to choose from than the other ram circles.
If there were no restrictions on moving rams between ram circles, selection scenario 3 would be a good option as it is the only scenario optimizing the Norwegian Cheviot population as a whole. However, because of the restriction of moving animals between ram circles, selection scenario 3 is not applicable today.
Across ram circle selection of AI rams with pre-selected test- and elite rams (scenario 4) is the selection method that is closest to the selection process as it is practiced today. However, the contributions of AI-rams are small compared to the other rams and thus the effect of the OCS is small. This method has no significant difference in merit of rams compared to actual selection. Only one AI ram was selected with this method. This might indicate that on a total population scale, the rate of inbreeding is low when test and elite rams are selected by the ram circles, and thus one AI ram can be selected with a contribution of 8.2% without increasing the rate of inbreeding above 1% per generation. Scenario has a high amount of ewes mated per ram compared to the other scenarios as seen in Table 15. The reason for this is probably that there are only 70 selected rams in total when the other scenarios had between 80 and 92 selected rams.
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Scenario 4 requires that the test- and elite rams are selected before the AI rams. In the actual selection scheme, the AI rams are selected in July and the test- and elite rams are selected in October. Selecting elite and test rams before AI rams makes this scenario difficult to
implement because elite rams are awaiting information about slaughtered offspring that is included in the fall index-runs, and the test rams are awaiting the phenotypical evaluation.
Genomic selection could be a solution for this particular issue. However, the Norwegian Cheviot is a small breed and genomic selection is not likely to be applied anytime soon.
Surprisingly, the highest average O-index was obtained using the within ram circle selection method (scenario 1), and not in the across ram circle selection method (scenario 3). All animals were selected with OCS in both selection methods, but the expectation was that optimization on the larger population (across ram circle selection) would give the highest average O-index. However, there is no significant difference in the average merit between the animals selected in the two scenarios.
As shown in Table 12, there is a significant difference in the ram EBV between actual selection (scenario 0) and the within- and across ram circle selection scenarios (scenario 1 and 3)(p<0.001). There is also a significant difference at 1% level between actual selection and the within ram circle with preselected AI rams selection method (scenario 2). There is no significant difference in average ram O-index between the actual selection and the across ram circle selection of AI rams with pre-selected test- and elite rams (scenario 4).
The results show that the highest increase in genetic gain would come from selecting all the rams with OCS. However, the cost of having AI rams is so high that having more AI rams than what is used today is not realistic. When the AI rams are pre-selected, it will still be an advantage of using OCS for selection of test and elite rams. The only restriction with this method is that the ram circles boards would lose the privilege to select rams.
In this study, the ewes were pre-selected based on information of lambing in 2015. When applying OCS in a real selection process, the information about ewes are not available with certainty. However, in a similar study by Olsen et al. (2013) on Nordic cold-blooded trotter horses, it was suggested to base the selection on the ewes used the previous year. The study showed only minor differences between stallions selected with the mares from the previous year, compared with the stallions selected with the actual mated mares the same year (Olsen et al. 2013).This approach could be used when applying OCS in the Norwegian Cheviot
33 Population. However, the turnover of mares are probably lower than the turnover of ewes, which is around 25% per year.
Another issue with the results from these selection methods is that the same cmax is used for all rams and classes. Another suggestion is to use different cmax on the different age-classes in order to select a suitable amount of rams in each age-class. However, it seems as though putting many restrictions on the OC selection with Gencont 2 gives a lower outcome in the genetic progress or average EBV. This is probably because it have less room to select the best candidates and give them a higher contribution than the lower candidates. The best way to approach applying OCS would be to restrict the selection as little as possible in order to get the best optimization of the selection.
For this study, a measure of amount of increase in genetic gain for the different selection scenarios is not calculated. The reported amount of increase in genetic gain using OCS vs.
truncation selection with BLUP is very variable, and finding out the specific increase of genetic gain for the Norwegian Cheviot population can be difficult as it depends on different factors. Meuwissen (1997) reports of between 21 to 27% compared to BLUP selection with same level of inbreeding using simulated data and even as much as 60% higher when the inbreeding restriction was lower. Meuwissen and Sonesson (1998) reports that using a dynamic selection rule to allow for overlapping generations in the optimal selection calculations yielded as much as 44% higher selection response than BLUP selection.
The difference in selection response is higher in small populations and with lower constraints on the rate of inbreeding (Meuwissen & Sonesson 1998). Gandini et al. (2014) used
simulations of young bull schemes to compare OCS with truncation selection. They report a difference in genetic gain between 2% and 6.3%. They also agreed with the results from Meuwissen and Sonesson (1998) that OCS is more favorable with lower rates of inbreeding (Gandini et al. 2014). Gourdine et al. (2012) simulated OCS in local pig breeds to select for meat quality. The results showed that the genetic gain using OCS was lower than that of truncation selection with BLUP. However, the study was not comparing the genetic gain on the same level of inbreeding, as the cumulative level of inbreeding for the BLUP selection increased substantially, the OC selection stayed low, but still allowed for an increase in genetic gain of between 69% and 98% of the BLUP selection (Gourdine et al. 2012). Based on these studies, one can assume that applying OCS in the Norwegian Cheviot population would restrict in the very least, the rate of inbreeding to the desired rate while maintaining the same genetic progress as is today. However, the increased O-index in the selected rams with
34
selection method 1, 2 and 3, indicate that there might be room to increase the genetic progress in the Norwegian Cheviot Sheep by applying OCS in the selection scheme.
Four selection scenarios were applied to the Norwegian Cheviot population with promising results. However, the scenarios tested have some limits that could be solved with further testing of different selection scenarios. In these selection scenarios, all ram type categories were given the same cmax, but i.e. AI rams have a higher capacity in terms of matings per ram than rams used with natural service. One suggested selection scenario could be to apply different cmax for different ram type categories. Other suggested scenarios are i.e. with only natural service rams, without elite rams and higher amount of AI, or scenarios involving OC selection of ewes
.
35
6 Conclusion
Using OCS to select rams for the Norwegian Cheviot population will give a higher average O-index among the selected rams compared to the selection currently done, leading to genetic gain and keeping the rate of inbreeding below one percent.
None of the four selection scenarios is however able to fulfill all the current biological, economical or structural restrictions. The large AI-usage and post-selection of rams for AI across the ram circles are disadvantages for scenario 1. The manual pre-selection of rams for AI is a limit for scenario 2, as well as the selection of rams for natural mating by OCS instead of ram circle boards that is a limitation to scenario 1, 2 and 3. The prohibition by the
Norwegian Food Safety Authority to move ram circles between ram circles limits Scenario 3.
In scenario 4, pre-selecting test- and elite rams before selecting AI rams with OCS is limited by insecure breeding values.
Solutions to overcome these limits can be to use more AI in the Norwegian Cheviot
population to accommodate scenario 1. If adaptions to the software makes it possible change the manual pre-selection of AI ram candidates into selecting 2-4 AI rams with OCS, scenario 2 is more applicable. Educating the ram circle boards and flock owners to understand the benefits of OCS selection is also a solution to the issue of changing the selection structure in scenario 1, 2 and 3. As long as there is limitations against moving animals between ram circles by Norwegian law, scenario 3 does not have a realistic application. If genomic selection is implemented, a higher security for the O-index could be achieved and this could solve the limits of selection method 4. However, using genomic selection in such a small population is not realistic as of today.
A modification of selection scenario 2, where the AI selection with OCS is limited to 2-4 rams, and the ram circle board agrees to use OCS for test- and elite ram selection is recommended.
Even so, implementing OCS in the selection scheme requires change. NSG, the ram circles and the flock owners need to discuss and decide possible changes in the current selection procedure to find out how to implement OCS in order to benefit the Norwegian Cheviot population in the best manner possible.
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7 References
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I
Appendices
Appendix 1
Within ram circle selection of all ram type categories (scenario 1).
II
Within ram circle selection with pre-selected AI rams (scenario 2).
Ram Circle 141
III
I Appendix III
Selected rams for the Across ram circle selection (scecnario 3).
II
Selection of AI rams based on pre-selected test- and elite rams (scenario 4).
Ram
III Appendix V
Rams with tagged offspring in 2015.
Ram Circle 141
*Animal ID was not found for some animals that were not included in the dataset. Their Pheontypic Evaluation ID was used instead.
Ram Circle 161
*Animal ID was not found for some animals that were not included in the dataset. Their Pheontypic Evaluation ID was used instead.
Ram Circle 163
IV
*Animal ID was not found for some animals that were not included in the dataset. Their Pheontypic Evaluation ID was used instead.
Ram Circle 171
*Animal ID was not found for some animals that were not included in the dataset. Their Pheontypic Evaluation ID was used instead.