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Table 3 Estimates of parameters related to the residual variance of weight traits in Nellore beef cattle

From: Genetic and environmental heterogeneity of residual variance of weight traits in Nellore beef cattle

Trait/Sex*

Model**

σ² Av,exp

σ² m,exp

dAIC

dBIC

σ² Av

Ev(%)

h²v(%)

rmv

GBW/M

HOM

0.0165

0.549

0.19

8.2

372

8.54

0.13

0.23

(0.02)

(0.09)

GBW/M

HET

1.07

0.297

−276

−285

21500

65.20

5.87

0.21

(0.19)

(0.11)

GBW/F

HOM

2E-7

0.105

2

10

0.00

0.03

0.00

0.12

(0.00)

(0.08)

GBW/F

HET

0.495

0.0614

−121

−129

7030

43.80

3.37

0.26

(0.10)

(0.09)

GBW/B

HOM

0.0404

0.265

−16

−7.1

730

12.90

0.39

0.27

(0.02)

(0.04)

GBW/B

HET

0.476

0.189

−350

−340

8570

43.70

3.89

0.27

(0.07)

(0.05)

YW/M

HOM

2E-7

 

2

9.1

0.01

0.03

0.00

0.12

(0.00)

 

YW/M

HET

1.68

 

−240

−230

44400

78.70

5.50

−0.01

(0.32)

 

YW/F

HOM

0.0277

 

0.83

8.2

881

10.60

0.21

0.21

(0.03)

 

YW/F

HET

0.457

 

−79

−71

14200

42.70

3.02

0.36

(0.11)

 

YW/B

HOM

0.0193

 

−0.002

8.2

740

8.77

0.13

0.25

(0.02)

 

YW/B

HET

0.473

 

−195

−187

17300

43.00

2.89

0.23

(0.08)

 
  1. *GBW = weight gain from birth to weaning; YW = long-yearling weight; M = males; F = females; B = both sexes; **HOM = assuming homogeneous residual variance in model (1); HET = model allowing for differences between sire families in residual variance. σ²Av,exp: estimated additive genetic variance for log squared of estimated residuals, ln(ê²); σ²m,exp: estimated maternal additive genetic variance for ln(ê²); σ²Av:estimates of additive genetic variance on the scale of the residual variance (σ²e), assuming the quantitative model for genetic heterogeneity of σ²e[5]; Ev: evolvability of σ²e in %; h²v: heritability of σ²e; rmv: Pearson’s correlation between sire EBV for mean and for σ²e; AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; dAIC (dBIC): difference between the AIC (BIC) obtained for a model considering additive genetic effects on ln(ê²) and the AIC (BIC) of a model only with fixed effects; negative values indicate that better fit was obtained with the model considering genetic heterogeneity on ln(ê²); standard errors are presented between brackets.