1) Consider the following traits and genetic parameters in dairy cattle:
(heritability on, genetic correlations above, phenotypic correlations below diagonal; phenotypic st.dev., repeatability and economic values below)
Protein Fat Mastitis SCS
Protein Yield per lactation 0.25
Fat Yield per lactation 0.75
Mastitis cases per lactation -0.20
Somatic Cell Score -0.25
0.85
0.30
-0.20
-0.25
0.10
0.15
0.08
0.50
0.15
0.20
0.80
0.15
Phenotypic st. dev.
Economic value/unit
44 kg
3 $/kg
55 kg
2 $/kg
0.5 cases/lact 1.25 score units
-50 $/case - - -
Somatic cell score (SCS) is a logarithmic transformation of the number of somatic cells in milk per ml.
SCS is used as an indicator trait for mastitis incidence (higher SCS means more white blood cells are present in the udder to fight an infection) but has no direct economic value. The reason for using SCS instead of mastitis incidence records for selection is that it is easy to record in the field through milk recording (SCS can be measured in the same milk sample that is already used to measure protein and fat content of milk). Data on mastitis incidence on the other hand, is much more difficult to collect in an objective manner. In addition, SCS has higher heritability.
NOTE: You can use the Selection Index Program (SIP) for the calculations below. Note that you can
‘trick’ the program to give you responses for traits that are NOT in the breeding goal by including them with an economic value = 0. For some calculations you’ll need to use a program that can do matrix manipulations, such as MATLAB or EXCEL.
We are interested in developing an economic selection index for sires based on the average performance of 50 of the sire’s daughters (1 record per daughter and each daughter has a different dam) for protein yield, fat yield, and SCS in first lactation. Initially, assume that there are NO records on mastitis. a) Using SIP, derive the optimal index and its accuracy for selection of sires for a breeding goal that includes protein yield, fat yield, and mastitis.
Consider the average performance of 50 daughters of the following two sires (all averages are corrected for fixed effects, i.e. deviated from the mean):
Protein Fat SCS
Sire A
Sire B
+35 kg
+18 kg
+31 kg
+25 kg
+0.4
- 0.8 b) Compute the selection index value for these two sires. c) What is the expected difference in protein yield, fat yield, mastitis incidence, SCS, and profit per cow per lactation between daughters of these two sires if they are mated to the same dams.
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d) Predict the genetic superiority of the top 10% of sires based on the index derived under a) for protein yield, fat yield, mastitis, somatic cell score, and profit per cow per lactation e) Using the SIP, and assuming that it is possible to get records on mastitis incidence on each daughter, investigate and discuss the following questions for the scenario outlined above (i.e. 50 daughter records per trait): i) Assuming for the moment that the cost of recording mastitis incidence is equal to the cost of recording SCS, would it be better to record mastitis incidence instead somatic cell score? ii) Determine the value of recording mastitis incidence AS WELL AS somatic cell score on responses to selection in profit and the individual traits. f) Now, consider that there is some uncertainty about the genetic correlation between Mastitis and SCS and that the real genetic correlation may not be 0.8 but only 0.65. Compute the efficiency of the index you derived under a) (i.e. records on mastitis are not available) for the case where the correlation is only
0.65. NOTE: you should be able to get all the matrices you need for this calculation from the Selection
Index Program but will need to enter them into a program that can handle matrices (e.g. EXCEL). Based on this result, would you recommend SCS to be included in the index for selection of sires? Explain your answer. g) Now consider that sires are evaluated using a Multiple-trait Animal model that includes all records on protein yield, fat yield, SCS. Using the procedure of Schneeberger et al. (1992) (see notes), derive the weights that should be put on an index that includes multiple-trait EBV for protein yield, fat yield, and
SCS. Note: you cannot do this by SIP but will need to use a program such as EXCEL to do the necessary matrix manipulations.
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