View Full Table | Close Full ViewTable 1.

Timing of 4 BW recordings in Katanning Resource Flock from 2000 to 2005

 
Traits1
Year WT1 WT2 WT3 WT4
2000 10 Jan. 23 Feb. 30 May 27 Sept.
2001 16 Jan. 23 Feb. 6 May 25 Sept.
2002 15 Jan. 26 Feb. 3 June 8 Oct.
2003 13 Jan. 26 Feb. 3 June 7 Oct.
2004 13 Jan. 23 Feb. 17 May 7 Oct.
2005 11 Jan. 25 Feb. 18 May 3 Oct.
Average 13 Jan. 24 Feb. 23 May 2 Oct.
Avg days from start of year 13 55 143 274
1WT1 = premating BW; WT2 = postmating BW; WT3 = prelamb BW; WT4 = weaning BW.



View Full Table | Close Full ViewTable 2.

Mean and SD of BW 1 to 4, BW loss (LOSS) and BW gain (GAIN) of ewes aged 2, 3, and 4 yr old

 
Trait1 Mean, kg SD, kg
WT1 age = 2 yr 50.2 6.24
WT1 age = 3 yr 58.6 7.09
WT1 age = 4 yr 61.7 7.30
WT2 age = 2 yr 48.0 6.46
WT2 age = 3 yr 58.0 6.45
WT2 age = 4 yr 60.7 6.62
WT3 age = 2 yr 50.3 6.04
WT3 age = 3 yr 58.5 6.77
WT3 age = 4 yr 60.9 7.13
WT4 age = 2 yr 56.9 7.41
WT4 age = 3 yr 61.7 8.13
WT4 age = 4 yr 63.7 8.70
LOSS age = 2 yr –2.23 2.73
LOSS age = 3 yr –0.606 3.95
LOSS age = 4 yr –0.968 3.79
GAIN age = 2 yr 6.55 7.20
GAIN age = 3 yr 3.14 7.20
GAIN age = 4 yr 2.83 7.41
1WT1 = premating BW; WT2 = postmating BW; WT3 = prelamb BW; WT4 = weaning BW.



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Akaike’s information criterion (AIC) and the Bayesian-Schwarz information criterion (BIC) for multivariate and random regression analysis of BW at ages 2, 3, and 4 yr

 
Item Age 2 yr Age 3 yr Age 4 yr
Log likelihood
    Multivariate –12,836 –11,295 –8,675
    Random regression –12,862 –11,331 –8,679
ΑΙC1
    Multivariate 25,713 22,630 17,389
    Random regression 25,757 22,685 17,443
BIC1
    Multivariate 25,851 22,764 17,517
    Random regression 25,875 22,799 17,552
1Low AIC and BIC values are preferred.



View Full Table | Close Full ViewTable 4.

Additive genetic [σ2a (WT2 – WT1)] and residual plus permanent environmental [σ2e (WT2 – WT1)] variance of BW loss (LOSS) calculated using the variance for each BW and covariance between BW estimated using multivariate analysis of BW and random regression analyses1

 
Additive genetic σ2a WT1 σ2a WT2 cova(WT2,WT1) σ2a (WT2 – WT1)
    Age = 2 yr
           Multivariate 17.7 (1.79) 15.5 (1.62) 16.2 (1.61) 0.89 (0.26)
           Random regression 18.5 (1.73) 16.6 (1.65) 17.3 (1.68) 0.63 (0.23)
    Age = 3 yr
           Multivariate 19.7 (2.74) 20.2 (2.50) 19.1 (2.50) 1.69 (0.56)
           Random regression 20.5 (2.64) 19.7 (.46) 19.7 (2.49) 0.79 (0.48)
    Age = 4 yr
           Multivariate 24.5 (3.64) 22.5 (3.13) 22.9 (3.21) 1.24 (0.65)
           Random regression 23.2 (3.37) 22.1 (3.11) 22.4 (3.18) 0.42 (0.35)
Residual + permanent environmental σ2e WT1 σ2e WT2 cove(WT2,WT1) σ2e (WT2– WT1)
    Age = 2 yr
           Multivariate 9.16 (1.17) 9.17 (1.08) 6.49 (1.05) 5.35 (0.28)
           Random regression 8.74 (1.09) 8.54 (1.01) 5.83 (1.05) 5.62 (0.27)
    Age = 3 yr
           Multivariate 20.5 (2.11) 14.0 (1.78) 12.6 (1.81) 9.24 (0.58)
           Random regression 20.0 (2.09) 14.7 (1.72) 12.4 (1.77) 9.81 (0.54)
    Age = 4 yr
           Multivariate 17.9 (2.75) 13.9 (2.30) 11.1 (2.34) 9.69 (0.72)
           Random regression 19.4 (2.67) 14.6 (2.29) 11.8 (2.33) 10.3 (0.64)
1For example, the additive genetic variance was estimated using . Permanent environmental variance was only estimated for the random regression. Covariances cova and cove are additive genetic (a) and residual (e) covariances. WT1 = premating BW and WT2 = postmating BW.



View Full Table | Close Full ViewTable 5.

Estimates of additive (σ2a LOSS) and phenotypic variance (σ2p LOSS) and heritability (h2) for BW loss (LOSS) with SE (in parentheses) estimated using multivariate analysis of BW, random regression, and the BW change trait analyses

 
Method Age2 σ2a LOSS σ2p LOSS1 h2
Multivariate 2 0.89 (0.26) 6.24 (0.20) 0.14 (0.04)
Random regression 2 0.63 (0.23) 6.06 (0.20) 0.10 (0.04)
BW change trait 2 0.86 (0.26) 6.24 (0.21) 0.14 (0.04)
Multivariate 3 1.69 (0.56) 10.9 (0.41) 0.15 (0.05)
Random regression 3 0.79 (0.48) 10.6 (0.40) 0.07 (0.03)
BW change trait 3 1.51 (0.55) 10.8 (0.41) 0.14 (0.05)
Multivariate 4 1.24 (0.65) 10.9 (0.47) 0.11 (0.06)
Random regression 4 0.42 (0.35) 10.7 (0.48) 0.04 (0.03)
BW change trait 4 1.23 (0.65) 10.9 (0.47) 0.11 (0.06)
1The phenotypic variance of LOSS was calculated by adding the additive, residual and permanent environmental variances from Table 4.
2Age measured in years.



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Additive genetic (σ2a (WT4 – WT3)) and residual plus permanent environmental [σ2e (WT4 – WT3)] variance of BW gain (GAIN) calculated using the variance for each BW and covariance between BW estimated using multivariate analysis of BW and random regression1

 
Additive genetic σ2a WT3 σ2a WT4 cova(WT3,WT4) σ2a (WT4 – WT3)
    Age = 2
        Multivariate 18.4 (1.81) 21.0 (2.59) 15.8 (1.88) 7.73 (1.31)
        Random regression 15.9 (1.60) 19.9 (2.01) 16.0 (1.68) 3.62 (0.83)
    Age = 3
        Multivariate 19.8 (2.48) 26.7 (3.67) 20.4 (2.67) 5.95 (1.55)
        Random regression 19.6 (2.35) 23.5. (2.85) 20.3 (2.44) 2.63 (1.13)
    Age = 4
        Multivariate 20.5 (3.27) 27.5 (4.42) 21.4 (3.38) 5.24 (1.78)
        Random regression 21.8 (3.07) 24.5 (3.70) 21.2 (3.12) 3.87 (1.42)
Residual + permanent environmental σ2e WT3 σ2e WT4 cove(WT3,WT4) σ2e (WT4 – WT3)
    Age = 2
        Multivariate 9.08 (1.17) 19.9 (1.87) 6.61 (1.25) 15.8 (1.11)
        Random regression 10.5 (1.08) 17.5 (1.78) 6.03 (1.09) 15.9 (1.09)
    Age = 3
        Multivariate 14.9 (1.80) 24.5 (2.76) 8.78 (1.90) 21.9 (1.51)
        Random regression 15.7 (1.60) 24.3 (2.51) 8.91 (1.71) 22.2 (1.51)
    Age = 4
        Multivariate 19.3 (2.57) 26.5 (3.48) 11.0 (2.58) 23.8 (1.85)
        Random regression 17.3 (2.33) 28.5 (3.14) 10.9 (2.32) 24.0 (1.86)
1For example, the additive genetic variance was estimated using . Permanent environmental variance was only estimated for the random regression. Covariances cova and cove are additive genetic (a) and residual (e) covariances. WT3 = prelambing BW; WT4 = weaning BW. Ages are in years.



View Full Table | Close Full ViewTable 7.

Estimates of additive genetic (σ2a GAIN) and phenotypic variance (σ2p GAIN) and heritability (h2) for BW gain (GAIN) with SE (in parentheses) estimated with multivariate analysis of BW, random regression, and the BW change trait methods

 
Method Age, yr σ2a GAIN σ2p GAIN h2
Multivariate 2 7.73 (1.31) 23.5 (0.83) 0.33 (0.05)
Random regression 2 3.62 (0.83) 19.5 (1.24) 0.18 (0.04)
BW change trait 2 7.92 (1.32) 23.5 (0.84) 0.33 (0.05)
Multivariate 3 5.95 (1.55) 27.8 (1.05) 0.21 (0.05)
Random regression 3 2.63 (1.13) 24.9 (1.75) 0.11 (0.04)
BW change trait 3 4.89 (1.37) 27.6 (1.03) 0.18 (0.05)
Multivariate 4 5.24 (1.78) 29.1 (1.25) 0.18 (0.06)
Random regression 4 3.87 (1.42) 27.8 (2.1) 0.14 (0.05)
BW change trait 4 5.71 (1.68) 29.3 (1.26) 0.19 (0.05)



View Full Table | Close Full ViewTable 8.

Estimates of genetic correlations between BW loss (LOSS) and BW gain (GAIN; rg LOSS GAIN) with SE in brackets estimated with multivariate analysis of BW, random regression and the BW change trait analyses

 
Method Age, yr cov(LOSS,GAIN)1 rg LOSS GAIN
Multivariate 2 –0.00 (0.42) –0.00 (0.16)
Random regression 2 –0.59 (0.25) –0.47 (0.13)
BW change trait 2 0.08 (0.43) 0.03 (0.16)
Multivariate 3 –1.32 (0.66) –0.42 (0.19)
Random regression 3 –1.27 (0.42) –0.87 (0.21)
BW change trait 3 –1.33 (0.66) –0.42 (0.20)
Multivariate 4 –0.09 (0.76) –0.03 (0.30)
Random regression 4 –0.75 (0.50) –0.57 (0.24)
BW change trait 4 –0.06 (0.75) –0.02 (0.30)
1Estimated using cova(WT2 – WT1,WT4 – WT3) = cova(WT1,WT3) + cova(WT2,WT4) – cova(WT1,WT4) – cova (WT2,WT3). Covariance cova is the additive genetic covariance. WT1 = premating BW; WT2 = postmating BW; WT3 = prelambing BW; WT4 = weaning BW.



View Full Table | Close Full ViewTable 9.

Genetic correlations between ages for BW loss (LOSS) and BW gain (GAIN; ±SE in parentheses)

 
LOSS
GAIN
Item Age 3 yr Age 4 yr Age 3 yr Age 4 yr
Age 2 yr 0.34 (0.24) 0.39 (0.30) 0.53 (0.14) 0.51 (0.15)
Age 3 yr 0.13 (0.32) 0.99 (0.15)