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Sheep Improvement Limited lamb birth fate (BFATE) codes

 
BFATE code Description
E Embryo transfer progeny
F Fostered
H Hand reared
J Born dead
J3 Died within 3 d of birth (autopsy)
K Died between birth and rearing
L AI progeny
M Died of misadventure
P Born dead – premature (autopsy)
R Born dead – rotten (autopsy)
1 Died between rearing and weaning
4 Culled at birth (alive but not tagged)



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Description of the 6 models used to estimate variance components

 
Model2
Type Effect1 A1 A2 A3 B LogA LogB
Fixed class effect Flk
BY
BR
AOD
Sex
Flk × BY
Flk × BR
BY × BR
BR × AOD
Flk × BY × BR
Fixed covariates BDAY × Flk × BY
Random effect Direct genetic
Maternal genetic
cov(a, m)
BDAYc × Flk × BY
Litter
Phenotypic transformation Logit
1Flk = flock; BY = year of birth class; BR = birth rank class; AOD = age of dam class; BDAY = day of birth; cov(a, m) = covariance between direct and maternal genetic effects; BDAYc = day of birth classified in periods of 2 wk. Litter is the random litter effect (dam/year) and logit is the logit transformation of phenotype.
2A1 = model with no day of birth fitted; A2 = model where BDAY × Flk × BY is fitted as fixed covariates; A3 = model where BDAYc × Flk × BY is fitted as random effects; B = same as A3 but with an extra random litter effect fitted; LogA = model A3 but with phenotypes transformed using a logit transformation; LogB = model B with logit transformation.



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Cross-frequencies of the new lamb survival trait (NEWSUR) and the current Sheep Improvement Limited lamb survival trait (SILSUR; and conditional percentages, per row/column if appropriate)

 
SILSUR status
NEWSUR status Dead Alive Missing Total
Dead 388,993 (99.6%/96.3%) 10,901 (0.4%/2.7%) 4,187 (9.4%/1.0%) 404,081 (11.8%)
Alive 701 (0.2%/0.0%) 2,644,398 (88.4%/99.3%) 18,784 (42.0%/0.7%) 2,663,883 (77.7%)
Missing 840 (0.2%/0.2%) 336,980 (11.3%/93.7%) 21,712 (48.6%/6.0%) 359,532 (10.5%)
Total 390,534 (11.4%) 2,992,279 (87.3%) 44,683 (1.3%) 3,427,496



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Estimates of variance components and genetic parameters (with their SE in parentheses), proportion, and restricted log-likelihood for the new lamb survival trait (NEWSUR) using different linear models

 
Model2
Item1 A1 A2 A3 B
(BDAYc) 0.007 (0.002) 0.007 (0.003)
(litter) 0.014 (0.002)
(e) 0.109 (0.002) 0.108 (0.002) 0.108 (0.002) 0.098 (0.002)
(a) 0.007 (0.003) 0.006 (0.002) 0.005 (0.002) 0.005 (0.003)
(a, m) –0.001 (0.003) –0.001 (0.002) –0.001 (0.002) –0.002 (0.002)
(m) 0.008 (0.003) 0.008 (0.003) 0.007 (0.002) 0.002 (0.002)
(y) 0.121 (0.003) 0.119 (0.003) 0.126 (0.004) 0.123 (0.004)
(a) 0.055 (0.000) 0.052 (0.000) 0.043 (0.000) 0.040 (0.002)
(m) 0.064 (0.000) 0.064 (0.000) 0.055 (0.000) 0.019 (0.001)
(a, m) –0.186 (0.003) –0.212 (0.003) –0.136 (0.003) –0.438 (0.003)
(g) 0.012 (0.003) 0.011 (0.003) 0.011 (0.003) 0.004 (0.002)
(g) 0.097 (0.000) 0.092 (0.000) 0.085 (0.000) 0.035 (0.000)
LnL –1,564,964.02 –1,562,810.38 –1,558,802.22 –1,555,568.89
1 (BDAYc) = estimated variance of the day of birth (classified in periods of 2 wk) × flock × year of birth effect; (litter) = estimated variance of the litter effect; (e) = estimated residual variance; (a) = estimated direct additive genetic variance; (a, m) = estimated covariance between direct and maternal effects; (m) = estimated genetic maternal additive genetic variance; (y) = estimated phenotypic variance; (a) = estimated direct heritability; (m) = estimated maternal heritability; (a, m) = estimated genetic correlation between direct and maternal effects; (g) = estimated total genetic (direct + maternal) variance; (g) = estimated total heritability; LnL = restricted log-likelihood.
2A1 = model with no day of birth fitted; A2 = model where the interaction between effects day of birth, flock, and year of birth is fitted as fixed covariates; A3 = model where the interaction between day of birth classes, flock, and year of birth is fitted as random effects; B = same as A3 but with an extra random litter effect fitted.



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Estimates of variance components and genetic parameters (with their SE in parentheses), and restricted log-likelihood for the new survival trait (NEWSUR) using 2 different logistic models (LogA and LogB)

 
Model2
Item1 LogA LogB
(BDAYc) 0.358 (0.015) 0.347 (0.015)
(litter) 0.355 (0.010)
(e) 3.290 (0.000) 3.290 (0.000)
(a) 0.128 (0.007) 0.083 (0.006)
(a, m) 0.006 (0.010) 0.002 (0.007)
(m) 0.315 (0.012) 0.141 (0.010)
(y) 4.102 (0.019) 4.220 (0.019)
(a) 0.031 (0.002) 0.020 (0.002)
(m) 0.077 (0.003) 0.033 (0.002)
(a, m) 0.028 (0.052) 0.023 (0.068)
(g) 0.454 (0.013) 0.229 (0.011)
(g) 0.111 (0.003) 0.054 (0.003)
LnL −1,454,703.73 −1,402,085.36
1 (BDAYc) = estimated variance of the day of birth (classified in periods of 2 wk) × flock × year of birth effect; (litter) = estimated variance of the litter effect; (e) = estimated residual variance; (a) = estimated direct additive genetic variance; (a, m) = estimated covariance between direct and maternal effects; (m) = estimated genetic maternal additive genetic variance; (y) = estimated phenotypic variance; (a) = estimated direct heritability; (m) = estimated maternal heritability; (a, m) = estimated genetic correlation between direct and maternal effects; (g) = estimated total genetic (direct + maternal) variance; (g) = estimated total heritability; LnL = restricted log-likelihood.
2LogA = model where the interaction between day of birth classes, flock, and year of birth is fitted as random effects and where phenotypes are transformed using a logit transformation; LogB = same as model LogA but with an extra random litter effect fitted.