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Course: Genetics and Breeding (12226)

Course: Genetics and Breeding (12226). Faculty of Veterinary Medicine An-najah National University Instructor: Dr. Jihad Abdallah Selection Methods and selection response. Individual selection, Family selection and within-family selection.

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Course: Genetics and Breeding (12226)

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  1. Course: Genetics and Breeding (12226) Faculty of Veterinary Medicine An-najah National University Instructor: Dr. Jihad Abdallah Selection Methods and selection response

  2. Individual selection, Family selection and within-family selection • Individual selection: selection of individuals based on their own phenotypic values (also called mass selection). • Family selection: whole families are selected or rejected based according to the phenotypic mean of the family (we select the family with the largest mean). This method is preferred when the heritability of the trait is low. But the efficacy of this method is reduced when the environmental variation common to the members of the same family is large. • Sib-selection and progeny testing are family-selection methods. • Within-family selection: the best individual in each family is selected. The main case in which this method has an advantage is when there is a large common environmental variance.

  3. EXAMPLE: The following table shows phenotypic values of 16 individuals in four families of full-sibs. Individual selection: we select A1, B1, A2, and D1 or B2 Family selection: we choose all 4 individuals in family A Within-family selection: we select A1, B1, C1 or C2, and D1

  4. Selection Response • Selection response is the expected rate genetic change (genetic progress or improvement) which results from selection. • The effectiveness of selection is measured by the rate of genetic change achieved.

  5. Per year

  6. Selection intensity • Selection intensity (i) is the selection differential expressed in standard deviations. • Selection differential (S): the difference between the mean selection criterion of selected animals and the mean selection criterion of all potential parents. • Selection criterion (SC): the information on which we base selection (breeding value, phenotypic value, etc).

  7. Truncation selection: Point of truncation

  8. Selection intensity increases as the proportion selected is reduced

  9. With phenotypic selection (individual selection or mass selection) the formula for selection response can be expressed as: Note that with phenotypic selection, accuracy = h

  10. Generation Interval • Generation interval is the amount of time required to replace one generation with the next. • More practical definition: the average age of parents when their offspring are born. • As the generation interval increases the rate of genetic change is reduced.

  11. Average generation interval Species Male Female Cattle 3 to 4 4.5 to 6 Sheep 2 to 3 4 to 4.5 Swine 1.5 to 2 1.5 to 3 Horses 8 to 12 8 to 12 Chickens 1 to 1.5 1 to 1.5 .

  12. Selection intensity, generation interval and the additive variance may differ between males and females. • This should be accounted for in the equation for selection response

  13. EXAMPLE 1:Suppose we select for six-month weight in lambs based on phenotypic values. If the average of all candidates for selection is 32 kg, the phenotypic standard deviation is 4 kg, the generation interval in sheep is three years and the heritability of six-month weight is 0.40, suppose also that the average of selected parents is 42 kg. Find: • The intensity of selection • The expected selection response per year. Solution: This corresponds to an improvement at a rate 4.16% per year

  14. EXAMPLE 2: Phenotypic selection for yearling weight in beef cattle. Bulls: keep the top 3% Cows: keep the top 50% Suppose heritability is 0.40, generation interval is 5 years for bulls and 6.5 years for cows and the additive genetic variance is 1225 in both sexes. Find the expected selection response per year. i = 2.27 for males and 0.80 for females

  15. Genetic Correlation and Correlated Response • Genetic correlation (rG): is the strength of the relationship between breeding values for one trait and breeding values for another trait. • Genetic correlation ranges from -1.0 to +1.0 • Genetic correlation is a measure of “pleiotropy”. • Pleiotropy is when the same gene (or genes) affect more than one trait.

  16. Correlated Response • Correlated response is the genetic change in one trait due to selection on another trait and caused by presence of genetic correlation between the two traits.

  17. The expected correlated response in trait Y caused by selection practiced on trait X is calculated as: Where, rG is the genetic correlation between traits X and Y rAIx: is the accuracy of prediction of breeding values for trait X ix : is the selection intensity for trait X : is the additive genetic standard deviation for trait Y With phenotypic selection, the formula can be expressed as: Phenotypic standard deviation for trait Y Heritability for trait Y Heritability for trait X

  18. Example: suppose we are selecting dairy bulls for improving milk yield using progeny testing and we are using the top 5% of bulls with average accuracy of 0.90. What is the expected genetic change in fat% given that the genetic correlation between milk yield and fat% is –0.50, the heritability of fat% is 0.50 and the phenotypic variance for fat% is 0.80. Suppose also that the generation interval is 6 years.

  19. Selection for multiple traits • So far we have dealt with selection for a single traits. However, in practice we may want to improve several traits. • There are three methods of selection for multiple traits: • Tandem selection • Independent culling levels • Economic selection index

  20. Tandem Selection • In this method we begin by selection for one trait, after several years we start selecting for the second trait and so forth until the desired level of improvement is reached. • This method is not efficient: - it takes long time - if unfavorable genetic correlations exist between selected traits, selection for one will negatively affect one or more of the other traits.

  21. Independent culling levels • A standard (a culling level) is set for each trait. • Selected animals should meet the standard for each trait. • Any animal which does not meet the standard of any one of the traits is culled even if it is excellent for one or more of the traits. • Faster than the tandem method, but it is very strict (may cause culling of excellent animals for one trait which do not meet the standards for other traits).

  22. Selection Index • Combines information on several traits in a single index using weighting factors. • Where : • b1, …………,bn are weighting factors (partial regression coefficients) • P1: information on trait 1 • . • . • . • Pn: information on trait n

  23. Advantages • More efficient than tandem selection and independent culling methods. • It takes into account the genetic correlations between traits. • It takes into account the economic value for each trait. • An animal’s excellence in one trait can compensate for its weakness in another trait.

  24. The selection index is a prediction of the aggregate breeding value “H”. H is also called the breeding objective. Where H is the aggregate breeding value measured in monetary units (dollar, euro, dinar, etc) a1,a2,…………,an  are economic weights for traits 1,2 …….n. The economic value: the amount of change in profit (in monetary units) due to change by one unit in breeding value of the trait.

  25. Construction of selection indices • Construction of selection indices requires estimates of the genotypic and phenotypic variances and covariances for traits included in the index. • Using these estimates in conjunction with economic weights in construction of indices is straightforward.

  26. We will use matrix notation to develop the economic selection index using n traits: P = n x n phenotypic variance-covariance matrix for the n traits, b = n x 1 column vector of partial regression coefficients to be solved for, G = n x 1 column vector of genetic variances and covariances between traits , a = a vector of economic weights for traits. Pb = Ga, and solving the equations for the partial regressions yields:

  27. Example for two traits: Yearling weight (YW) and birth weight (BW) in beef cattle: P b G a The solution for b is obtained as: Suppose VP1=3600, VP2=100, VA1 = 1440, VA2 = 40, a1 = 1.10, a2 = - 6.52, cov(P1,P2) = 210, cov(A1,A2) =168 

  28. Application of the index to beef cattle data (the numbers are in lbs):

  29. In practice, the selection index that is widely used is the one which combines the the economic weights with the genetic predictions calculated with BLUP methodology. As we have seen, the breeding objective is: So, And therefore, Economic weights Predicted breeding values for traits 1 to n for the same animal

  30. For the previous example:

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