Multi-Trait analysis of growth traits: fitting reduced rank models using principal components for Simmental beef cattle

Mota, Rodrigo Reis; Costa, Edson Vinícius; Lopes, Paulo Sávio; Nascimento, Moysés; Silva, Luciano Pinheiro da; Silva, Fabyano Fonseca e; Marques, Luiz Fernando Aarão

doi:10.1590/0103-8478cr20150927

Cited by 1 publication

(2 citation statements)

References 14 publications

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“…After a varimax rotation of the component matrix, only four PC were extracted with eigenvalues equal to or higher than one. The extraction of only four PC allows us to better understand the complex correlations among traits, as well as the use of more parsimonious models (Mota et al, 2016). Legarra et al (2004) reported that more parsimonious models require smaller computational demands and are less susceptible to numerical errors.…”

Section: Journal Of Animalmentioning

confidence: 99%

“…As mentioned above, four PC contributed for 68.7% of the total variation between recorded body traits. Hence, we performed a stepwise multiple regression analysis using one, two, three and four PC (4 models In this case, the selection index would have four weighted coefficients which will decrease computational demands (Pinto et al, 2006;Mota et al, 2016).…”

Section: Component Scoresmentioning

confidence: 99%

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Principal Component Analysis for Body Weight Prediction of Corriedale Ewes from Southern Peru

Canaza-Cayo¹,

Mota²,

Amarilho-Silveira³

et al. 2021

JAHP

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We aimed to verify the relationship between body measurements (BM) and body weight as well as to investigate the prediction of live weight (LW) by using original BM and principal component scores of Corriedale ewes. BM of 100 ewes collected in the Illpa Experimental Centre of the National University of Altiplano in Peru were used. Data were recorded on LW, wither height (WH), rump height (RH), thoracic perimeter (TP), abdominal perimeter (AP), fore-shank length (FSL), fore-shank width (FSW), fore-shank perimeter (FSP), tail width (TW), tail perimeter (TPe), hip width (HW), loin width (LWi), shoulder width (SW), forelimb length (FL) and body length (BL). Pearson correlation and principal component analysis (PCA) were applied to LW and others BM. Additionally, regression equations of LW on BM and on its principal components (PC) were computed. Models were compared by using coefficients of multiple determinations (R 2 ), Akaike information (AIC), Bayesian information (BIC) criteria and root mean squared error (RMSE). Correlations (r) for all BM with LW were positive and significant (r = 0.20 -0.78), except for FSW (r = 0.18). The PCA of BM and LW extracted four components explaining 68.7% of the total variance. The prediction LW model by using four PC had the lowest RMSE, AIC and BIC values as well as the highest R 2 compared to models with smaller number of PC or based on original measurements. Our results suggested that this approach is a feasible alternative to predict LW.

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Section: Journal Of Animalmentioning

confidence: 99%

Section: Component Scoresmentioning

confidence: 99%

Principal Component Analysis for Body Weight Prediction of Corriedale Ewes from Southern Peru

Canaza-Cayo¹,

Mota²,

Amarilho-Silveira³

et al. 2021

JAHP

View full text Add to dashboard Cite

show abstract

Multi-Trait analysis of growth traits: fitting reduced rank models using principal components for Simmental beef cattle

Abstract: Multi-Trait analysis of growth traits: fitting reduced rank models using principal components for Simmental beef cattle.Ciência Rural, v.46, n.9, set, 2016. 1656Multi-Trait analysis of growth traits: fitting reduced rank models using principal components for Simmental beef cattle

Cited by 1 publication

References 14 publications

Principal Component Analysis for Body Weight Prediction of Corriedale Ewes from Southern Peru

Principal Component Analysis for Body Weight Prediction of Corriedale Ewes from Southern Peru

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