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Genetic evaluation of an index of birth weight and yearling weight. Michael MacNeil USDA Agricultural Research Service Miles City, Montana. I = yearling weight - 3.2(birth weight) Proposed by Dickerson et al. (1974). Objectives.
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Genetic evaluation of an index of birth weight and yearling weight Michael MacNeil USDA Agricultural Research Service Miles City, Montana
I = yearling weight - 3.2(birth weight) Proposed by Dickerson et al. (1974)
Objectives • Estimate genetic parameters for birth, weaning, yearling, and mature weights of CGC • Evaluate genetic responses resulting from selection based on the index
Selection Lines • Established in 1989 • Control line (n = 912) • Index line (n = 950) • Generation intervals • Control line 3.90 0.08 yr • Index line 3.16 0.04 yr • 3 generations • 212 kg selection differential
CGC population • Stabilized composite of ½ Red Angus, ¼ Charolais, and ¼ Tarentaise
Phenotypes • Birth weight (n = 5,083) • 200-d weight (n = 4,902) • 365-d weight & index (n = 4,626) • Cow weight (n = 1,433; 4,375 obs)
Derivative-free multiple-trait REML • Calf traits • fixed contemporary groups • random direct & maternal additive effects • uncorrelated random maternal permanent environmental effects • Cow weight • fixed contemporary group effects, • random direct additive effects, • uncorrelated random permanent environmental effects
Three Sets of Analyses • a single-trait analysis of the index • four 2-trait analyses of the index with birth weight, 200-d weight, 365-d weight, and cow weight • three 2-trait analyses of 365-d weight with birth weight, 200-d weight, and cow weight.
Relationship of Index to Weight Traits& Response Versus to Selection for 365-d wt
Implications • Despite a genetic antagonism that compromises selection response for reduced birth weight and increased postnatal growth, favorable genetic responses can be achieved. • Selection for the index favorably affected the shape of the growth curve, restricting response in birth weight and mature weight of cows. • Selection intensity in experiment was reduced relative to that which would be available across a breed using national cattle evaluation and AI.