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

Mazza, Serena; Guzzo, Nadia; Sartori, Cristina; Mantovani, Roberto

doi:10.1017/s1751731115001883

Cited by 10 publications

(23 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The total variance for PCA was higher than 49.74% (2 components) reported by Guiterrez and Goyache (2002) and lower than 65.77% (7 components) reported by Roughsedge et al (2000) for 10 and 23 type traits, respectively. For FA, Mazza et al (2016a) selected 6 factors with eigenvalues greater than 1 for both Rendena and Aosta Red Pied cattle accounting for 63% (20 type traits) and 58% (22 type traits) of the total variability in the 2 dual-purpose breed, whereas Mantovani et al (2005) used 23 type traits and selected 7 factors with eigenvalues greater than 1 accounting for 63% total variability. The total variations in the aforementioned studies were higher than the total variability recorded in our study.…”

Section: Pca and Famentioning

confidence: 99%

“…The aggregation of these traits in selection index may be hampered by collinearity between some traits due to a coherent (co)variance matrix (Macciotta et al, 2012). The PCA and FA are 2 statistical approaches that can be used to avoid analyzing large numbers of highly correlated traits and explore the relationship between traits with minimal loss of information (Schneider and Fikse, 2007;Mazza et al, 2016a). In this way, correlated traits could be loaded in the same PC or latent factor with each including traits with common biological or physiological characters, or both (Ali et al, 1998).…”

Section: Pca and Famentioning

confidence: 99%

“…This suggests that the multivariate techniques are more efficient than the measured phenotypes in implementing aggregate selection index for breeding purpose. However, because the expected response to selection depends on the signs of the loadings, the knowledge of the loadings of the phenotypic traits that each PC and latent factors underlies allows more accurate interpretation of the expected trait variation (Mazza et al, 2016a).…”

Section: Pca and Famentioning

confidence: 99%

See 2 more Smart Citations

Genetic parameter estimates for body conformation traits using composite index, principal component, and factor analysis

Olasege

Zhang

Zhao

et al. 2019

Journal of Dairy Science

View full text Add to dashboard Cite

Information about genetic parameters is population specific and it is crucial for designing animal breeding programs and predicting response to selection. This study was carried out to estimate the genetic parameters for 23 body conformation traits of 45,517 Chinese Holstein reared in Eastern China from 1995 to 2017 with the Bayesian inference method using a linear animal mixed model. The methods to integrate these traits included (1) using the composite index from the Dairy Association of China and (2) applying principal component analysis and factor analysis to explore the relationship between the conformation traits. Estimates of heritability using the composite index were low (0.04; feet and legs) to moderate (0.23; body capacity). Strong genetic correlations were observed between the individual body conformation traits. Both principal components (1 to 7; eigenvalues ≥ 1) and latent factors (1 to 7; eigenvalues ≥ 1) explained 60.37% of total variability. Principal component 1 and factor 1 accounted for the traits that are usually associated with milk production. Moderate to low heritability were estimated through multi-trait analysis for principal components (from 0.07 to 0.21) and latent factors (from 0.07 to 0.23). Genetic correlations among the 2 multivariate techniques are typically lower compared with the one existing among the measured traits. Results from these analyses suggest the possibility of using both principal component analysis and factor analysis in morphological evaluation, simplifying the information given by the body conformation traits into new variables that could be useful for the genetic improvement of the Chinese Holstein population. This information could also be used to avoid analyzing large number of correlated traits, thereby improving precision and reducing computation burdens to analyze large and complex data.

show abstract

Section: Pca and Famentioning

confidence: 99%

Section: Pca and Famentioning

confidence: 99%

Section: Pca and Famentioning

confidence: 99%

See 1 more Smart Citation

Genetic parameter estimates for body conformation traits using composite index, principal component, and factor analysis

Olasege

Zhang

Zhao

et al. 2019

Journal of Dairy Science

View full text Add to dashboard Cite

show abstract

“…The main derived factors typically include correlated traits and may assume a biological meaning (Ali et al, 1998). Factor analysis has been widely studied as a tool for genetic evaluation of type traits in some Italian cattle, both in specialized beef breeds (Chianina, Marchigiana, and Romagnola;Forabosco et al, 2005), and in local dual-purpose cattle as Rendena and Aosta Red Pied Mazza et al, 2016). Dwelling on the milk yield, current genetic evaluation largely uses single test day (TD) records to enable earlier selection decisions (Bilal and Khan, 2009) and to improve selection accuracy respect to the traditional 305-d lactation yields (Schaeffer et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Genetic correlations between type and test-day milk yield in small dual-purpose cattle populations: The Aosta Red Pied breed as a case study

Mazza

Guzzo

Sartori

et al. 2016

Journal of Dairy Science

View full text Add to dashboard Cite

This study aimed at estimating the relationships between linear type traits and milk production in the dual-purpose Aosta Red Pied (ARP) cattle breed, by expressing type traits as factor scores with the same biological meaning of the individual traits. Factor analysis was applied to individual type traits for muscularity and udder of 32,275 first-parity ARP cows, obtaining 3 factor scores for individual muscularity (F1), udder side (F2), and udder conformation (F3). Data from 169,008 test-day records of milk, fat, and protein yield (kg), belonging to the first 3 lactations of 16,605 cows, were also analyzed. After obtaining genetic parameters for both morphological factors and milk production traits through a series of AIREML single-trait models, bivariate analyses were performed on a data set accounting for 201,283 records of 35,530 cows, to assess the phenotypic and genetic correlations among all factor scores and milk yield traits. The heritability estimates obtained proved to be moderate for both groups of traits, ranging from 0.132 (fat) to 0.314 (F1). Muscularity factor showed moderate and negative genetic correlations (ra) with udder size (-0.376) and udder conformation (0.214) factors. A low and negative ra was found between udder factors. Strong and positive ra were found among all the 3 milk production traits and F 0010 (ra≥0.597). Negative ra with milk traits were obtained for both F 0005 and F3, ranging from -0.417 to -0.221. Phenotypic correlations were lower than the genetic ones, and sometimes close to zero. The antagonism between milk production and meat attitude traits suggests that great attention should be paid in assigning proper weight to the traits, comprising functional traits such as udder conformation, included in selection indices for the dual-purpose breed. The ra obtained for factor scores are consistent with previous estimates for the corresponding individual type traits, and this confirms the possible use of factor analysis to improve type traits relevant to beef attitude.

show abstract

“…There are some studies in this field; Kern et al (2014) found two factors for Brazilian Holstein cattle, Chu and Shi (2002) reported four factors for Holstein cows, while Mazza et al (2016) found six factors for Rendena and Aosta Red Pied dualpurpose Italian breeds.…”

Section: Discussionmentioning

confidence: 99%

Principal component analysis of conformation traits in Hungarian Simmental cows

Török¹,

Komlósi²,

Béri³

et al. 2021

CZECH J ANIM SCI

View full text Add to dashboard Cite

The aim of the current research was to analyze the linear type traits of Hungarian Simmental dual-purpose cows scored in the first lactation using principal component analysis and cluster analysis. Data collected by the Association of Hungarian Simmental Breeders were studied during the work. The filtered database contained the results of 8 868 cows, born after 1997. From the evaluation of main conformation traits, the highest correlations (r = 0.35, P < 0.05) were found between mammary system and feet and legs traits. Within linear type traits, the highest correlation was observed between rump length and rump width (r = 0.81, P < 0.05). Using the principal component analysis, main conformation traits were combined into groups. There were three factors having 84.5 as total variance ratio after varimax rotation. Cluster analysis verified the results of the principal component analysis as most of the trait groups were similar. The strongest relationship was observed between feet and legs and mammary system (main conformation traits) and between rump length and rump width (linear type traits).

show abstract

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

Cited by 10 publications

References 24 publications

Genetic parameter estimates for body conformation traits using composite index, principal component, and factor analysis

Genetic parameter estimates for body conformation traits using composite index, principal component, and factor analysis

Genetic correlations between type and test-day milk yield in small dual-purpose cattle populations: The Aosta Red Pied breed as a case study

Principal component analysis of conformation traits in Hungarian Simmental cows

Contact Info

Product

Resources

About