The impact of genotyping strategies and statistical models on accuracy of genomic prediction for survival in pigs

Journal of Animal Science and Biotechnology

Research

Guosheng Su¹  Ole F. Christensen¹  Mogens Sandø Lund¹  Bjarne Nielsen²  Tianfei Liu³ 

[1] Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark;Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark;Pig Research Centre, SEGES, 1609, Copenhagen, Denmark;Institute of Animal Science, Guangdong Academy of Agricultural Sciences, 510640, Guangzhou, China;Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark;

关键词: Genomic prediction;    Genotyping strategy;    Simulation;    Statistical models;    Survival;   

DOI  :  10.1186/s40104-022-00800-5

 received in 2022-05-31, accepted in 2022-11-20,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundSurvival from birth to slaughter is an important economic trait in commercial pig productions. Increasing survival can improve both economic efficiency and animal welfare. The aim of this study is to explore the impact of genotyping strategies and statistical models on the accuracy of genomic prediction for survival in pigs during the total growing period from birth to slaughter. ResultsWe simulated pig populations with different direct and maternal heritabilities and used a linear mixed model, a logit model, and a probit model to predict genomic breeding values of pig survival based on data of individual survival records with binary outcomes (0, 1). The results show that in the case of only alive animals having genotype data, unbiased genomic predictions can be achieved when using variances estimated from pedigree-based model. Models using genomic information achieved up to 59.2% higher accuracy of estimated breeding value compared to pedigree-based model, dependent on genotyping scenarios. The scenario of genotyping all individuals, both dead and alive individuals, obtained the highest accuracy. When an equal number of individuals (80%) were genotyped, random sample of individuals with genotypes achieved higher accuracy than only alive individuals with genotypes. The linear model, logit model and probit model achieved similar accuracy.ConclusionsOur conclusion is that genomic prediction of pig survival is feasible in the situation that only alive pigs have genotypes, but genomic information of dead individuals can increase accuracy of genomic prediction by 2.06% to 6.04%.

【 授权许可】

CC BY   
© The Author(s) 2022

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