We use large phenotype and pedigree data sets in different livestock species to quantify the genetic variation in economically important traits, related to e.g. conformation in horses, reproduction in fur animals or behaviour in dogs. In dairy cattle, bioeconomic models are used to analyze selection priorities among traits. The economic weights of the traits can be adjusted by farmers’ intended and realized preferences to increase the acceptance of the outcome. The efficiency of selection schemes is optimized in terms of data collection volume or the number of genotyped animals, which define the level of achievable accuracy and selection intensity. The development of selection programs is done in collaboration with breeding organisations.
Links to Project Pages
- FoodAfrica: Improved food and nutritional security from better utilisation of dairy cattle breed types in Senegal
- ReiGN: Reindeer husbandry in a Globalizing North – resilience, adaptations and pathways for actions
Paakala, E., Martín‐Collado, D., Mäki‐Tanila, A. and Juga, J. 2018. Variation in the actual preferences for AI bull traits among Finnish dairy herds. Journal of Animal Breeding and Genetics 135:410–419. https://doi.org/10.1111/jbg.12359
Fon Tebug, S., Missohou, A., Sabi, S. S., Juga, J. K., Poole, E. J., Tapio, M. and Marshall, K. 2018. Using body measurements to estimate live weight of dairy cattle in low-input systems in Senegal. Journal of Applied Animal Research 46:87-93. https://doi.org/10.1080/09712119.2016.1262265.
Hietala, P. and Juga, J. 2017. Impact of including growth, carcass and feed efficiency traits in the breeding goal for combined milk and beef production systems. Animal 11:564-573. https://doi.org/10.1017/S1751731116001877.
Peura J., Viksten, S., Kempe, R., Strandén, I. and Mäki-Tanila, A. 2017. Weight-for-length index as a measure of obesity in blue foxes. NJF Fur Animal Congress, Oslo, 5 p.
Bouquet, A.E., Sorensen, A.C. and Juga, J. 2015. Genomic selection strategies to optimize the use of multiple ovulation and embryo transfer schemes in dairy cattle breeding programs. Livestock Science 174:18-25. https://doi.org/10.1016/j.livsci.2015.01.014.
Hill, W.G. and Mäki-Tanila, A. 2015. Expected influence of linkage disequilibrium on genetic variance caused by dominance and epistasis on quantitative traits. Journal of Animal Breeding and Genetics 132:176-186. https://doi.org/10.1111/jbg.12140.
Hietala, P., Wolfová, M., Wolf, J., Kantanen, J. and J, Juga. 2014. Economic values of production and functional traits, including residual feed intake, in Finnish milk production. Journal of Dairy Science 97:1092-1106. https://doi.org/10.3168/jds.2013-7085.
Makgahlela, M.L., Mäntysaari, E.A., Strandén, I., Koivula, M., Nielsen, U.S., Sillanpää, M.J. and Juga, J. 2013. Across breed multi-trait random regression genomic predictions in the Nordic Red dairy cattle. Journal of Animal Breeding and Genetics 130:10–19. https://doi.org/10.1111/j.1439-0388.2012.01017.x.
Suontama, M., Van der Werf, J.H.J., Juga, J. and Ojala, M. 2013. Genetic correlations for foal and studbook traits with racing performance and implications for selection strategies in the Finnhorse and Standardbred trotter Journal of Animal Breeding and Genetics 130:178–189. https://doi.org/10.1111/j.1439-0388.2012.01011.x.