A Study of Genomic Prediction of 12 Important Traits in the Domesticated Yak (Bos grunniens)

Animals

Jie Pei¹  Xiaoming Ma¹  Qinhui Lei¹  Xian Guo¹  Min Chu¹  Ping Yan¹  Donghai Fu¹  Congjun Jia¹  Chunnian Liang¹  Xiaoyun Wu¹  Pengjia Bao¹  Xuezhi Ding¹  Zhiping Wen² 

[1] Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Science, Lanzhou 730050, China;Gannan Provincial Institute of Animal Science, Gannan 747000, China;

关键词: domesticated yak (bos grunniens);    genomic prediction;    growth performance;    disease resistance;    prediction accuracy;   

DOI  :  10.3390/ani9110927

来源: DOAJ

【 摘 要 】

The aim of this study was to explore the possibility of applying GP to important economic traits in the domesticated yak, thus providing theoretical support for its molecular breeding. A reference population was constructed consisting of 354 polled yaks, measuring four growth traits and eight hematological traits related to resistance to disease (involved in immune response and phagocytosis). The Illumina bovine HD 770k chip was used to obtain SNP information of all the individuals. With these genotypes and phenotypes, GBLUP, Bayes B and Bayes Cπ methods were used to predict genomic estimated breeding values (GEBV) and assess prediction capability. The correlation coefficient of the association of GEBV with estimated breeding value (EBV) was used as PA for each trait. The prediction accuracy varied from 0.043 to 0.281 for different traits. Each trait displayed similar PAs when using the three methods. Lymphocyte counts (LYM) exhibited the highest predictive accuracy (0.319) during all GP, while chest girth (CG) provided the lowest predictive accuracy (0.043). Our results showed moderate PA in most traits such as body length (0.212) and hematocrit (0.23). Those traits with lower PA could be improved by using SNP chips designed specifically for yak, a better optimized reference group structure, and more efficient statistical algorithms and tools.

【 授权许可】

Unknown