Proc. Assoc. Advmt. Anim. Breed. Genet. Vol 14
441
IMPLICATIONS OF DATA QUALITY ON THE ACCURACY OF ESTIMATED BREEDING VALUES FOR WEANING WEIGHT IN SHEEP
D.J. Brown1, B. Tier1 and R. Banks2
1 Animal Genetics and Breeding Unit∗, University of New England, Armidale, NSW 2351
2 MLA, C/O Department of Animal Science, University of New England, Armidale NSW 2351
SUMMARY
Accuracy of selection is an important feature for all breeding programs and genetic evaluation systems. Accuracy of estimated breeding values (EBVs) depends on several important parts of data recording and management group structure. This paper illustrates the areas in which breeders can improve the accuracy of EBVs with specific emphasis on data structure and recording. A range of information was generated using the pedigree and observations details and averaged over each flock to compare with average flock accuracy for weaning weight. When only animals with both sire and dam recorded were examined the proportion of animals with both sire and dam unknown, total number of animals, average sire generation interval and the proportion of animals with sire and dam both known were the major determinants. Once full pedigree is recorded the number of animals recorded and pedigree depth become important determinants of accuracy. Breeders can significantly improve the accuracy of their weaning weight EBVs by simply recording more animals and pedigree information.
Keywords: EBVs, accuracy, data quality, pedigree.
INTRODUCTION
Most sheep breeders aim to achieve genetic gain in the major traits that influence profitability.
Genetic progress is influenced by the accuracy of estimated breeding values (EBVs) throughout time and by the selection decisions taken by the breeder. Accuracy of estimated breeding values is influenced by many factors including: the depth of pedigree and how long a flock has been performance recording, the size of management groups, the number of sires in each group and their number of effective progeny, and linkage with other management groups and flocks.
This paper illustrates the factors that are most related to accuracy of weaning weight EBVs in sheep.
This will illustrate the areas in which breeders can improve the accuracy of the EBVs with specific emphasis on data structure and recording. The quality of the data is assessed using pedigree information, linkage, effective progeny numbers and observation counts for weaning weight.
MATERIALS AND METHODS
Data originated from the LAMBPLAN Poll Dorset database. Weaning weight (Wwt) was chosen for investigation as it was the most widely recorded trait. Multiple trait accuracies were calculated using OVIS (Brown et al. 2000) which utilised the methods of Graser and Tier (1997). The average weaning weight accuracy for each flock was calculated by averaging the OVIS weaning weight EBV
∗ AGBU is a joint institute of NSW Agriculture and The University of New England
Proc. Assoc. Advmt. Anim. Breed. Genet. Vol 14
442
for all animals in each flock. While pedigree information was used from the entire breed, information about observations and management group structure was only investigated in flocks that had recorded Wwt. Contemporary group (CG) was defined using breed, flock, year of birth, year of measurement, management group and sex. The following information was generated for each flock;
1) Direct pedigree can be found in four combinations; sire and dam known, only sire known, only dam known and both sire and dam unknown. The percentage of animals within each flock with each of the four combinations of pedigree information was counted,
2) Numbers of animals, sires and dams in each flocks pedigree and the proportion with Wwts recorded,
3) Number of sires and progeny (total and effective) from sires bred in other flocks,
4) Mean and maximum number of sire and dam generations in the pedigree with and without Wwt observations,
5) Mean and maximum sire and dam generation interval, calculated from records with and without Wwt observations,
6) Proportion of total progeny that are effective,
7) Average number of progeny per sire (total and effective), 8) Average number of sires per CG, and
9) Average effective progeny in each CG.
Effective number of progeny for each sire was calculated using the formula;
NEF =
∑
j ( nj ( Nj – nj) / Nj )Where NEF = Number of effective progeny, nj = the number of progeny by the sire in group j, and Nj
= the total number of progeny in group j.
This information was generated for five situations: 1) all animals in the database, 2) only animals with at least sire or dam recorded, 3) only animals with sire and dam known, 4) only animals from 3 and born in 1999, and 5) only animals from 4 and in flocks with more than 750 animals recorded. All available pedigree information was used for each case, however details about observations and CG structure was only used where the animals met these criteria.
Statistical analysis. Stepwise multiple regression analysis (SAS 1990) was performed to identify the variables that explain the variation in accuracy. This analysis has the major benefit of accounting for all the correlations between the pedigree and CG characteristics as well as the relationship of each with accuracy. The stepwise multiple regression only included variables which had a significant (P<0.05) F statistic.
RESULTS AND DISCUSSION
Average flock accuracy increased from case 1 to 5 and ranged between flocks from 31 to 78%. The average accuracy for the 1999 drop animals from the flocks which recorded full pedigree was high and only ranged between 60 and 68% (Table 1). These analyses included data from approximately 1000 flocks in case 1 down to 65 flocks for case 5. The results from case 1, where all animals were examined, illustrated that 72.3% of the variation in accuracy between these flocks could be explained by the characteristics examined. The proportion of animals with both sire and dam unknown, total number of animals, average sire generation interval and the proportion of animals with sire and dam
Proc. Assoc. Advmt. Anim. Breed. Genet. Vol 14
443
both known were the major determinants explaining 54.0, 6.6, 3.2 and 2.3% of the variation in accuracy respectively. The remaining 6.4% was explained by 8 other variables. In case 2, where only animals with at least sire or dam recorded were examined, 52.5% of the variation in accuracy could be explained. The major causes were the maximum number of generations in the dam pedigree with Wwt observations, the proportion of progeny that were effective, the mean number of generations of dam pedigree with data, the mean number of generations of dam pedigree and the number of animals with sire known. Ten other variables were also selected by the stepwise regression however each contributed less than 3% of the variation in accuracy. The results from the stepwise regression analysis for cases 3 to 5 are shown in Table 2. The coefficients all have the expected sign except those for the number of animals with Wwt, mean number of generations of dam pedigree and average sire generation interval. These anomalies were a result of the close relationship between these characteristics and suggest that these characteristics need to be considered in combination.
Table 1. Means (Mn) and ranges in accuracy and each characteristic calculated
Case 3 Case 4 Case 5
Mn Range Mn Range Mn Range
Number of flocks 516 334 65
Average Wwt accuracy 58.2 31 to 78 60.0 30 to 68 64.4 60 to 68 Total number of progeny 342 1.0 to 3766 462 1.0 to 3766 1450 776 to 3766 Effective progeny (%) 45.8 0 to 88.1 50.3 0 to 88.1 70.1 49.1 to 88.1 No. of animals with Wwt 689 2.0 to 6646 167 1.0 to 834 349 81 to 834 Av No. G of sire ped 4.3 1.0 to 9.0 5.3 1.0 to 9.0 5.8 1.3 to 8.0 Av No G of dam ped 1.7 0 to 7.4 2.3 0 to 5.6 2.8 1.0 to 4.5 Av No G of dam ped with data 1.9 0 to 4.4 2.2 0 to 4.4 2.7 1.0 to 4.4 Max No G of sire ped 6.7 1.0 to 12.0 6.9 1.0 to 12.0 8.2 2.0 to 12.0 Max No G of dam ped with data 3.6 0 to 10.0 4.0 0 to 9.0 5.1 2.0 to 9.0
Av sire GI 3.0 1.1 to 30 3.1 1.0 to 31 2.9 1.3 to 4.5
Av sire GI with data 3.1 0 to 30.1 3.2 0 to 31.1 3.0 1.3 to 5.2 Max sire GI with data 6.7 0 to 49.5 5.4 0 to 49.5 6.5 2.2 to 16.0 Av No. progeny per sire 102 3 to 597 112 4.0 to 597 140 61 to 316 Av No. sires per CG 2.7 1.0 to 9.2 2.8 1.0 to 8.9 4.2 2.0 to 8.9 Av = average, ped = pedigree, No. = number, G = generations, GI = generation interval and CG = contemporary group
Cases 3 to 5 illustrate that once pedigree information is complete the major determinants of accuracy appear to be flock size and pedigree depth. An important component of pedigree structure also involves examining where the data lies in the pedigree. Having multiple generations of data greatly improves the accuracy of the estimated breeding values. Once pedigree information and Wwts are routinely collected, depth in the pedigree will automatically be created.
Proc. Assoc. Advmt. Anim. Breed. Genet. Vol 14
444
Table 2. The variation in accuracy (Acc) explained by and coefficient (Coeff) for each variable included in the stepwise to multiple regression analysis
Case 3 Case 4 Case 5
Characteristic
% of variation
in Acc
Coeff
% of variation
in Acc
Coeff
% of variation
in Acc
Coeff Total number of progeny 18.0% 0.004 28.7% 0.002 6.2% 0.001 Effective progeny (%) 5.6% 0.053 0.9% 0.034
No. of animals with Wwt 2.1% -0.002
Mean No. G of sire pedigree 1.3% 1.122 2.1% 0.588
Mean No G of dam pedigree 7.3% -6.826 8.8% -2.023 14.4% -3.309 Mean No G of dam ped with data 6.9% 9.765 11.9% 2.191 20.8% 5.671 Max No G of sire ped 2.4% -0.612
Max No G of dam ped with data 8.5% -0.741
Average sire GI 0.6% 4.750
Average sire GI with data 1.9% -4.397
Max sire GI with data 0.6% -0.082
Average No. progeny per sire 1.1% 0.008 0.9% 0.012 Average No. sires per CG 1.5% 0.927
Total 52.5% 46.6% 58.7%
No. = number, G = generations, GI = generation interval and CG = contemporary group
CONCLUSIONS
The type and quality of the data and pedigree recorded heavily influence accuracy of genetic evaluation. This small comparison of data from the Poll Dorset flock using LAMBPLAN highlighted significant differences in 3 areas: 1) Amount of pedigree (ie sire and dam versus little or no dam data), 2) Depth of pedigree (generations deep) with data, and 3) The number of animals recorded.
The flocks with higher average accuracy were not the best for all of these aspects suggesting that accuracy of EBVs can be achieved through different levels of recording depending on the specific makeup of the data recorded. However to produce consistently accurate EBVs all three of these areas should be addressed. This paper also clearly illustrates that breeders should adopt good recording procedures when using utilising modern genetic technology. Each aspect of the effectiveness of the breeding program is readily obtained from the LAMBPLAN database, and this example highlights the value of such auditing for breeders and service providers. To maximise the effectiveness of their breeding program, breeders will increasingly need to focus on the return on their investment in recording, both the amount and quality. The development of routine auditing of data quality and breeding value effectiveness for LAMBPLAN flocks is under way.
REFERENCES
Brown, D.J., Tier, B., Reverter, A., Banks, R. and Graser, H.U. (2000) Wool Tech. Sheep Breed. 48:
285.
SAS (1990) "SAS/STAT User’s Guide Version 6" 4th ed., SAS Institute, Cary, N.C.
Graser, H.U. and Tier, B. (1997) Proc. Assoc. Advmt. Anim. Breed. Genet. 12: 547.
