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A premise was tested that genotyping both surviving and dead pigs will realise more genetic gain in post-weaning survival (PWS) than genotyping only surviving animals. Stochastic simulation was used to estimate the rate of true genetic gain in different genotyping scenarios that differed in varying proportions of genotyping dead animals. Selection was for only PWS that had heritability of 0.02. Mortality was assumed 10%. The trait was controlled by 7,702 biallelic quantitative trait loci distributed across a 30 Morgan genome. We used 54,218 biallelic single nucleotide polymorphisms (SNPs) that were used in genomic prediction. Genotyping both surviving and dead animals realised 12 to 24% more genetic gain than genotyping only surviving animals. The power of detecting SNP effects increased when animals of extreme phenotypes are genotyped. Therefore, genotyping both surviving and dead pigs realised more genetic gain than genotyping only surviving animals

International audience; We evaluated the economic and environmental impacts of strategies that incorporated selection for pig feed:gain and dietary optimization based on a single or multiple objectives tailored to meet the population’s nutritional requirements, with the goal to optimize sustainable farm feed efficiency. The economic and environmental features of the strategy were evaluated using life cycle assessment (LCA) and bio-economic models. An individual trait-based LCA model was applied to evaluate global warming potential (GWP), terrestrial acidification potential (AP), freshwater eutrophication potential (EP), and land occupation (LO) of the combined genetics and nutrition optimization to produce 1kg of live pig weighing 120kg at the farm gate. A parametric individual trait-based bio-economic model was developed and applied to determine the cost breakdown, revenue and profit to be gained from a 120kg live pig at the farm gate. Applying the combined genetics and nutrition optimization, the individual performance traits of pigs from two genetic lines with contrasted levels of feed efficiency were simulated with InraPorc in response to diets formulated for least cost, least environmental impacts, or minimum combination of cost and environmental impacts objectives, and accounting for the nutritional requirements of each line. Significant differences in the environmental impacts (P < 0.0001) and profit (P < 0.05) between lines predicted the same reference diet showed that selection for feed efficiency (residual feed intake, RFI) in pigs improves pig production sustainability. When pig responses were simulated with their line optimized diets, except for EP, all the line environmental impacts were less (P < 0.05) than with the reference diet. The high correlations of feed conversion ratio (FCR) with the environmental impacts (> 0.82) and the profit (< -0.88) in both lines underline the importance of feed efficiency as a lever for the sustainability of pig production systems. Implementing combined genetics and nutrition optimization, the inherent profit and environmental differences between the genetic lines was predicted to be reduced from 23.4% with the reference diet to 7.6% with the diet optimized jointly for economic and environmental objectives (joint diet). Consequently, for increased pig sustainability, diet optimization for sustainability objectives should be applied to cover the specific nutritional requirements arising in the herd from the pigs’ genetic level.