Gene350 Animal Genetics Lecture 19 14 September 2009

Last Time Estimation of breeding values and accuracy Own performance Progeny testing Sibs Ancestors

Today Multiple trait selection Methods Selection intensity Correlations between traits

Multiple trait selection Genetic, environmental & Phenotypic correlations are required for efficient multiple trait selection Phenotypic correlation-2 traits such as height and weight can be correlated Genetic correlation- the genetic values for 2 traits are correlated Environmental correlation- the environmental effects on the 2 traits are correlated

Multiple trait selection The definition of a correlation is the covariance between 2 traits divided by the square root of the product of the variances of the 2 traits Phenotypic correlation = Genetic correlation = Environmental correlation = Additive genetic correlation

Multiple trait selection Response in correlated trait CR = Formula used to monitor what happens in other traits when we select for a single trait Sign of genetic correlation determines the direction of correlated change Selection intensity, phenotypic standard deviation and the heritabilities also determine the magnitude of the expected change

Multiple trait selection The relative selection progress for trait 2 by selection for trait 1 as compared to progress from direct selection for trait 2 is

Multiple trait selection economic efficiency in populations reproductive efficiency maternal ability growth feed efficiency body composition product quality selection objective is very complex .

Selection objective all traits that contribute to economic efficiency Selection criterion all traits that are evaluated for improvement of the selection objective not necessarily the same traits .

How might the selection objective and selection criterion have different traits? Some traits in objective might be difficult to measure expensive to measure strongly associated with other traits .

How to decide what traits to include? economic importance heritability standard deviation genetic correlations among traits phenotypic correlations among traits ease or expense of measurement .

Methods of multiple trait selection tandem independent culling levels selection index .

Tandem selection select for one trait at a time improve first trait until satisfied improve second trait until satisfied improve third trait until satisfied etc .

Tandem selection not very effective especially bad if adverse correlations among traits example ADG and BF in pigs select for  ADG   BF select for  BF   ADG .

Tandem selection Traits should be considered in order of their economic value Difficulties include: Genetic correlations must be known Correlations and heritabilities must remain the same over several generations of intense selection for one trait 3. Economic values are linear 4. Economic value does not change over time .

Independent Culling Levels establish culling criterion for each trait cull any individual that fails any criterion advantage can cull sequentially can follow biological development of animal. Selection will occur in stages corresponding to level of maturity

Independent Culling Levels disadvantage strength in one trait cannot make up for another weakness If traits are correlated, calculating expected response is difficult .

Selection index equation that combines all traits of importance H = v1X1 + v2X2 + ….. VnXn factors in calculation economic importance heritability standard deviation genetic correlations phenotypic correlations .

Selection index advantages most improvement in net merit strength in one trait can make up for weakness in another disadvantage should measure all traits on all selection candidates

Selection index Properties of selection index Maximizes the correlation between the true additive genetic merit and its predicted value (accuracy of evaluation) 2. Minimizes the average squared prediction error (minimizes prediction error) 3. Genetic gain is faster with this method than with any other 4. Probability of correctly ranking pairs of animals is maximized 5. Procedure is unbiased (average of prediction error for all animals is zero)

National Swine Improvement Federation L=number born alive minus the average number born alive W=21-day litter weight minus adjusted 21-day litter weight D=days to 250 pounds minus the average days to 250 pounds B=backfat minus the average backfat RECOMMENDED INDEXES SPI = 100 +6.5(L) + W MI = 100 + 6(L) + .4(W) - 1.6(D) - 81(B) TI = 100 -1.7(D) - 168(B) .

A practical approach set up loose culling levels eliminate obviously inferior cull sequentially culling levels for soundness eliminate problems after all traits measured selection index on remaining selection candidates