A variance component estimation approach to infer associations between Mendelian polledness and quantitative production and female fertility traits in German Simmental cattle

Table 5 Phenotypic and genetic correlations between polledness, test-day production traits, SCS and fertility traits

Trait	\(\user2{r}_{\user2{P}}\)	\(\user2{r}_{{\user2{g~v}}}\)(SE)	\(\user2{r}_{{\user2{g~a}}}\)(SE)
MY	− 0.004	− 0.003 (0.068)	− 0.005 (0.068)
F%	− 0.004	− 0.010 (0.056)	0.002 (0.055)
P%	− 0.005	− 0.010 (0.052)	0.003 (0.051)
SCS	− 0.015	− 0.048 (0.101)	0.001 (0.096)
NRR-56	− 0.020	− 0.093 (0.372)	0.002 (0.340)
DFS	− 0.019	− 0.029 (0.042)	/*
DO	− 0.017	− 0.120 (0.248)	0.002 (0.221)

Standard errors for the genetic correlations were calculated based on heritability estimates and corresponding standard errors according to [15]
\(r_{P}\): phenotypic correlation calculated based on variance estimates from the QTL models; \(r_{{g~v}}\) (SE): genetic correlation and standard errors (in brackets) calculated based on variance estimates for \(v\); \(r_{{g~a}}\) (SE): genetic correlation and standard errors (in brackets) calculated based on variance estimates for \(a\)
*Since the full bivariate model including \({\mathbf{A}}\) and \({\mathbf{A}}_{{\mathbf{v}}}\) for the trait DFS did not fully converge, we present the results for a model excluding \({\mathbf{A}}\) in the model for polledness; \(r_{{g~a}}\). cannot be estimated from this model.

ISSN: 1297-9686