Abstract. One important question about genomic evaluation is how distance between generations of individuals in reference population and selection candidates, would affect the accuracy of genomic estimated breeding value of selection candidates. There were two schemes in the present study. In first scheme, for each individual a genome consisting 30 chromosomes each with 100 equally spaced single nucleotide polymorphisms (SNPs) and in second scheme a genome consisting 3 chromosomes each with 1000 equally spaced SNPs was simulated. To generate enough linkage disequilibrium between loci, random mating for 50 generations was done in a finite population. In generation 51, population size was expanded to 250 individuals. This structure was continued until generation 55. Individuals in generation 55 were juvenile and did not have phenotypic records and were selection candidates. Heritability was assumed to be 0.3. Our results showed using information from more distant generations would decrease accuracy of genomic estimated breeding values of selection candidates but in scheme in which marker distance was 1 centimorgan, increasing generation number between reference population and selection candidates would decrease accuracy more than scheme in which marker distance was 0.1 centimorgan. According to our results using EBVs of reference population instead of phenotypic records would increase accuracy extremely.
The main objective of the livestock industry, as an economic production system, is to increase production efficiency through changes in performance and to increase economic productivity. Therefore, in designing genetic improvement programs for domestic animals, it is necessary to pay attention to recognizing the system of production and the factors affecting its performance and the profitability of systems, that is, revenues and costs. For estimation of market liquidity flow and economic returns, using a bio economic model, data on the revenues and costs was used of traditional and industrial cattle in Ardebil province during the years 2012-2016. The nourishment method based on the type of management was divided into two methods: traditional nourishment (in pasture) and industrial nourishment. The results of this study showed that the highest share of revenue and costs of nourishment units was related to milk sales and nutritional costs in both systems respectively. The investment risk level for industrial systems with different levels of milk production (high production, average production and low production) and the traditional system were estimated to be 0.032, 0.078, 0.030 and 0.013, respectively using standard deviation that these numbers represent the degree of deviation of the real result from the average result with medium returns which shows the high risk of investment in industrial dairy cattle compared to traditional dairy cattle. In both systems, the highest estimated relative significance was related to production traits, followed by survival and growth traits, respectively and the least value was related to reproductive traits.
This study aims to investigate the degree of bias resulted from ignoring Bulmer effect during the estimation of genetic and economic progress in progeny test and genomic selection programs. To this end, a deterministic approach based on gene flow method in a time horizon of 70 years was used. In this study, milk production was considered as the selection goal under a four-path selection strategy. In the progeny test, asymptotic genetic variance of sires and dams decreased by 67.59% and 64.97%, respectively. Also, in genomic selection program, asymptotic genetic variance in sires and dams decreased by 68.56% and 63.06%, respectively. The maximum reduction in genetic variance occurred in the first three generations. In the progeny test program, the bias of genetic progress per generation due to ignoring Bulmer effect was four times higher than genomic selection program, but this difference decreased significantly in the results of single round and continuous selection after 20 generations. Bulmer effect resulted in 51.64% and 44.62% reduction in the economic efficiency of progeny test and genomic selection, respectively. According to the results of this study, ignoring Bulmer effect in the investigations concerning comparison between progeny test and genomic selection seems to be unreasonable. Long-term selection has more severe effect on genetic and economic aspects of progeny test in comparison to genomic selection program via decreasing genetic variance.
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