Factor Analysis of Genetic Evaluations for Type Traits of Canadian Holstein Sires and Cows

Factor analysis for genetic evaluation of linear type traits in dual-purpose autochthonous breeds

“…Sieber et al (1988) found that factors with eigenvalues >1 explained 73.6% of the total variance in type score of Holstein dairy cows. A similar value was found for the first five latent factors in a study on Canadian Holstein (Ali et al, 1998).…”

“…Several studies indicate that the number of traits can be represented by fewer factors without a reduction in accuracy when describing the cow's conformation (Sieber et al, 1987;Ali et al, 1998;Forabosco et al, 2005). The primary interest of the present study lies in the algebraic sign and magnitude of the loading coefficients and in the percentage of the total variance explained by each factor.…”

“…Factor analysis was then computed separately for the two breeds using the raw data (Chu and Shi, 2002 FACTOR Procedure;SAS Institute Inc., 2009). The varimax rotation as described by Kaiser (1958) was carried out to obtain latent factors that accounted only for the traits with large absolute value of loadings (Ali et al, 1998, Macciotta et al, 2004and 2012. The varimax rotation method was chosen in order to obtain orthogonal loading coefficients that maximize the variance of the loadings, that is, to maximize independencies among factors (Kaiser, 1958).…”

“…Basically, factor analysis can be regarded as an analytical linear model that is much less restrictive than a traditional linear model with one or a few dependent variables (Enevoldsen et al, 1996). Ali et al (1998) presented four reasons for which applying factor analysis to type trait data is considered to be an important innovation: (1) summarizing information from the observed type traits into a few unobserved and relatively uncorrelated derived factors; (2) partitioning each trait response into a covariant and a specific part the variance of each component can be estimated; (3) grouping type traits such that correlated traits could be isolated in the same factor and each factor will include traits with common biological and/or physiological characters; (4) the magnitude of each loading in the factor pattern reflects the importance of each type trait within the derived factor. Previously, factor analysis was applied in scientific disciplines during most of the 20th century, like in socio-biology (Crawford and DeFries, 1978;Crawford and Anderson, 1989) and it has been also used in animal sciences to find indicators of management and production levels for dairy cattle herds (Enevoldsen et al, 1996), to evaluate relationships between longevity and type traits (Vukasinovic et al, 1997), to model the shape of the lactation curve (Macciotta et al, 2004), and to study the structure of relationships between milk yield, milk composition and milk coagulation properties (Macciotta et al, 2012).…”

Milk microbial composition of Brazilian dairy cows entering the dry period and genomic comparison between Staphylococcus aureus strains susceptible to the bacteriophage vB_SauM-UFV_DC4

“…In a second step, the single OTUs were grouped in a number of factor scores to be used as a covariate. Factor scores are the output of a factorial analysis 52 a technique for multivariate analyses able to remove redundant information from correlated traits (in this case, the OTUs) and synthesize the original traits in a smaller set of derived traits named 'factors' , each including traits with common biological and/or physiological meaning 53,54 . An exploratory factor analysis with Varimax rotation 55 was performed on OTUs abundance to retain an amount of 18 factors according to the criterion of eigenvalues ≥1 (i.e., Kaiser criterion) 56 .…”