Copy number variation (CNV) is currently accepted as a common source of genetic variation. It is reported that CNVs may influence the resistance to disease and complex economic traits, such as residual feed intake, muscle formation, and fat deposition in livestock. Cell adhesion molecule 2 (CADM2) is expressed widely in the brain and adipose tissue and can regulate body weight through the central nervous system. Growth traits are important economic traits for animal selection. In this study, we aimed to explore the effect of CADM2 gene copy number variants on yak growth traits. Here, two CNVs in the CADM2 gene were investigated using the quantitative polymerase chain reaction (qPCR), and the association of the CNVs with growth traits in yak was analyzed using statistical methods by SPSS software. Differences were considered significant if the p value was < 0.05. Statistical analysis indicated significant association of CADM2-CNV2 with the body weight of the Chinese Ashidan yak. A significant effect of CNV2 (p < 0.05) was found on body weight at 6 months. In CNV2, the gain-type copy number variation exhibited greater performance than the other variants, with greater body weight observed at 6 months (p < 0.05). To the best of our knowledge, this is the first attempt to investigate the function of CADM2-CNVs and their association with growth traits in animals. This may be a useful candidate marker in marker-assisted selection of yaks.

Genomic selection is a promising breeding strategy that has been used in considerable numbers of breeding projects due to its highly accurate results. Yak are rare mammals that are remarkable because of their ability to survive in the extreme and harsh conditions predominantly at the so-called “roof of the world”—the Qinghai–Tibetan Plateau. In the current study, we conducted an exploration of the feasibility of genomic evaluation and compared the predictive accuracy of early growth traits with five different approaches. In total, four growth traits were measured in 354 yaks, including body weight, withers height, body length, and chest girth in two early stages of development (weaning and yearling). Genotyping was implemented using the Illumina BovineHD BeadChip. The predictive accuracy was calculated through five-fold cross-validation in five classical statistical methods including genomic best linear unbiased prediction (GBLUP) and four Bayesian methods. Body weights at 30 months in the same yak population were also measured to evaluate the prediction at 6 months. The results indicated that the predictive accuracy for the early growth traits of yak ranged from 0.147 to 0.391. Similar performance was found for the GBLUP and Bayesian methods for most growth traits. Among the Bayesian methods, Bayes B outperformed Bayes A in the majority of traits. The average correlation coefficient between the prediction at 6 months using different methods and observations at 30 months was 0.4. These results indicate that genomic prediction is feasible for early growth traits in yak. Considering that genomic selection is necessary in yak breeding projects, the present study provides promising reference for future applications.