| Summary: | The use of gene expression profiling with selective breeding for improved disease resistance in Atlantic salmon was evaluated using a computer simulation model. Disease resistance was improved by selection for survival after challenge test, or by selection for predicted survival from analysis of the gene expression response of cells challenged in-vitro to disease, or a combination of both traits. As the correlation between gene expression level and survival is unknown and will be dependant on the prediction equation used, we modelled different levels of genetic and phenotypic correlation (r and r) between the two traits. Genetic response was evaluated under four different selection criteria: family breeding value from disease challenge tests (CRIT1), predictor of individual challenge test phenotype using gene expression profiling (CRIT2), a combination of criteria 1 and 2 (CRIT3), and, direct selection of disease challenge test survivors (CRIT4). Economic evaluation was performed using estimates and records from the industry, and accounting for the costs of gene expression profiling, under an opportunity cost model. The predictive ability and selection accuracy improved under CRIT2 and CRIT3 as r and r was increased. The best genetic response was achieved by using disease challenge tests to select the best families in combination with gene expression tests to select the best individuals within families (CRIT3). Disease resistance was doubled after 6-7 generations of selection, and varying the phenotypic and genetic correlation had a relatively small effect on the overall genetic response after 10 generations. Benefit-cost was positive under all scenarios. With 10 generations of selection under CRIT3 the model predicted a benefit-cost ratio of more than 17:1, total added value per kg of fish of 0.29 Euro/kg and a nominal economic effect on operating income of over 175 million Euros. CRIT3 was almost as profitable an option as CRIT1, providing the cost of gene expression testing was less than Euro 280/individual and r was greater than 0.3, was more profitable than CRIT2 under all scenarios and resulted in greater total added value and higher nominal effect on operating income than all other selection criteria.
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