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

文献类型： 外文期刊

第一作者： Liu, Tianfei

作者： Liu, Tianfei;Liu, Tianfei;Nielsen, Bjarne;Christensen, Ole F.;Lund, Mogens Sando;Su, Guosheng;Nielsen, Bjarne

作者机构：

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

期刊名称：JOURNAL OF ANIMAL SCIENCE AND BIOTECHNOLOGY （ 影响因子：6.175； 五年影响因子：6.853 ）

ISSN： 1674-9782

年卷期： 2023 年 14 卷 1 期

页码：

收录情况： SCI

摘要： Background: Survival 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. Results: We 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. Conclusions: Our 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%.

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