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19 Cards in this Set
- Front
- Back
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Selection Objective, Definition
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A.K.A. breeding objective/aim/goal, or selection goal.
The character, or trait(s) we wish to improve by selection. Chosen irrespective of ability to measure the specific trait. Examples: (1) greater milk yield, (2) more efficient lean meat prod'n, (3) dogs w/out clinical hip dysplacia. |
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Selection Objective; Clear Objective
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It is important that selection objective is clear, and defined carefully.
"Bigger cattle" might mean taller, heavier, fatter, or more muscular. --> Don't just want a large animal, want a large meaty animal (not lots of bone or fat) |
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Selection Objective; Profitability
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Most breeding objectives aim to improve profitability of animals.
In theory, the selection objective for profitability should include every heritable trait that influences the economic return (not enough info to do this, only most important traits are included)... In practice traits are combined and weighted based on their relative economic value. |
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Selection Objective; Combining Traits (Theory)
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A specific selection objective may involve several traits. The different components of an objective will vary in their economic value and are given grater or lesser weight according to their importance.
SO... (in statistical terms); selection objective = (relative economic value of trait 1 x trait 1) + (relative economic value of trait 2 x trait 2) --> Selection objective = a1Y1 + a2Y2 ... + anYn. |
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Selection Objective: Relative Economic Value
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Defined as the MARGINAL PROFIT resulting from a change of one unit in the trait while all other traits remain unchanged. It is the economic values RELATIVE to each other that are important.
A coefficient used to "weight" traits by relative profitability/value. |
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Selection Objective; Combining Traits (Example)
Pigs bred for increased growth rate & increased carcass lean content; traits have relative economic values of 5 and 2 respectively. Selection objective = ? |
SELECTION OBJECTIVE = (5 x increase in growth rate) + (2 x increase in carcass lean content)
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Selection Criterion, Definition
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Selection Criterion = The traits that will actually be measured, not necessarily the same as the objective. May also include information on relatives (full-sibs, half-sibs, or offspring).
Example; For growth & carcass quality, criterion might be = (1) weight at 20 weeks & (2) backfat depth. |
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Selection for more than 1 character (3 strategies)
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(1) Tandem Selection
(2) Independent Culling Levels (3) Selection Index |
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Multiple Trait Selection: Tandem Selection
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Involves selecting breeding stock on only one criterion for several generations, then selecting on the next criterion for several generations.
Inefficient and is rarely used. |
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Multiple Trait Selection: Independent Culling Levels
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Selecting only those animals that exceed a particular level of performance for each criterion. -->If an animal falls below the cut-off for any one criterion, irrespective of its performance on other criteria, it will not be selected.
Can be complicated. This strategy is only possible if all the traits in the selection objective can be measured in each individual. |
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Multiple Trait Selection: Selection Index
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The construction of an index, which results in a single aggregate value that weights the different selection criteria according to their relative importance.
The MOST EFFICIENT METHOD of combining different selection criteria. Constructing an index can be quite complicated. |
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Selection Index; Equation
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Traits that are to be measured = X1, X2...
Coefficients that give the most efficient response to selection are b1, b2... Selection index = b1X1 + b2X2 + ... + bmXm |
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Selection Index; Variance & Covariance (Phenotypic & Genetic)
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Estimating coefficients for multiple traits in a selection index (based on relative importance of each trait) = requires information about;
(1) PHENOTYPIC VARIANCES; how much phenotypic variation exists among animals (2) PHENOTYPIC COVARIANCE; whether phenotypic differences in one trait are associated with phenotypic differences in another trait in the selection index. (3) GENETIC VARIANCE; how much genetic variation exists among animals (4) GENETIC COVARIANCE; whether genetic differences associated w/ one trait are related to genetic differences in another trait in the selection objective. |
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Selection Index; Variance (equation)
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Variance is a measure of the variation in a trait.
Sum of (Xi - Mean of all observations) squared / (n - 1) Xi = value of each individual n - 1 = number of observations minus 1 In other words, the variance is the sum of the squares of each individual deviation from the mean of all the observations and dividing by the number of observations minus 1. |
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Selection Index; Covariance (equation)
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Covariance measures how much variation in one trait is associated with variation in another trait.
Sum of (Xi - mean of observations) (Yi - mean of observations) / (n-1) Xi (or Yi) = value of each individual trait n = number of observations In other words, the covariance is obtained by multiplying the deviation from the mean in trait 1 by the deviation from the mean in trait two and dividing by the number of observations minus 1. |
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Selection Index; Equation to Estimate Selection Index Coefficients
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Pb = Ga
P = Phenotypic variance (matrix of phenotypic variances & covariances in selection index) b = coefficients/weight to be determined G = Genetic variance (matrix of genetic covariances b/w traits in selection index and traits in selection criterion) a = relative economic value |
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Selection Index; Selection Index Coefficient Equation & heritability
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Pb = Ga
b = P^-1 Ga G = only one term in matrix of genetic covariances = ADDITIVE GENETIC VARIANCE (Va) a = Can set the economic value to 1 for a single trait P = only one phenotypic variance in the matrix = TOTAL VARIATION OF A TRAIT (Vp) ... b = Va / Vp b = h squared (aka heritability) |
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Selection Index Coefficient Equation; SAMPLE CALCULATION
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P phenotypic variance = 1
G additie genetic variance = 0.3 a Relative economic value = 1 b = P^-1 Ga b = heritability = 0.3 |
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Selection Index Calculation; 3 animals & 3 traits
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Remember; selection index = b1X1 + b2X2 + ...+bmXm
Animal 1: Trait measurements (Trait 1) 10, (2) 0, (3) -10 Animal 2: (1) 0, (2) -10, (3) 10 Animal 3: (1) -10, (2) 10, (3) 0 Coefficients; b1 = 1, b2 = 2, b3 = -1 (beneficial to reduce this trait) Animal 1, selection index value = 1(10) + 2(0) + -1(-10) = 20 Animal 2 = 1 (0) + 2 (-10) + -1 (10) = -30 Animal 3 = 1 (-10) + 2 (10) + -1 (0) = 10 Therefore; Animal 1 has highest score = which is an INDICATION OF PROFIT to expect from that animal. |
