Selection on an Index of Traits

1. X T I = b z = b z j j T T T T 2 æ = æ ( b z ; b z ) = b æ ( z ; z ) b = b P b I T T T T 2 æ = æ ( b z ; b z ) = b æ ( z ; z ) b = b G b A A A I 2 æ T b G b 2 A h = = I 2 I T æ b P b I Selection on an Index of Traits A common way to select on a number of traits at once is to base selection decisions on a simple index of trait values, The resulting phenotypic and additive-genetic variance for this synthetic trait are; This gives the resulting heritability of the index as

2. Class problem Enter G and P matrices from Example 33.1 in R. For Compute additive and phenotypic variance, and h2 for the index

3. X p T T { { G b - b G b b G b ° 1 T 2 S = b P R = G P S = G b = { ¢ p R = { h æ = { ¢ b P b = { ¢ p j k j k I I I æ æ T T T b P b I I b P b b P b k { S = P b æ I Thus, the response in the index due to selection is How does selection on I translate into selection on the underlying traits? Since R = GP-1S, the response vector for the underlying means becomes

4. Class problem For a selection intensity of i = 2.06 (upper 5% selected) compute the response in the index and the vectors of selection differentials and responses (S and R) for the vector of traits.

5. Changes in the additive variance of the index Bulmer’s equation holds for the index: We can also apply multivariate Bulmer to follow the changes in the variance-covariance matrix of the components:

6. This is done by selecting those individuals with the largest breeding value for this index, namely The Smith-Hazel Index Suppose we wish the maximize the response on the linear combination aTz of traits Hence, we wish to find a phenotypic index I = bTz So that the correlation between H and I in an Individual is maximized. Key: This vector of weights b is typically rather different from a!

7. Smith and Hazel show that the maximal gain is obtained by selection on the index bTz where The Smith-Hazel Index Thus, to maximize the response in aTz in the next generation, we choose those parents with the largest breeding value for this trait, H = aTg. This is done by choosing individuals with the largest values of I = bsTz.

8. We wish to improve the index In-class problem Compute the Smith-Hazel weights (using previous P, G) Compute the response for the SH index with truncation selection saving the upper 5% Compare this response to that selecting directly on I=aTz

9. Other indices Estimated index: Base-index: Elston (or weight-free) index: Heritability index : bi = a ih2i Retrospective index: since R = Gb, b = G-1R

10. Bending: Problem with Smith-Hazel Require accurate estimate of P, G! Values of P, G change each generation from LD! Dealing with negative eigenvalues of H = P-1G

12. Restricted and Desired-gains Indices Instead of maximizing the response on a index, we may (additionally) wish to restrict change in certain traits or have a desired gain in specific traits in mind Suppose for our soybean bean data, we wish no response in trait 1 Class Problem: Suppose weights are aT = (0,1,1), i.e, no weight on trait 1. Compute the response in trait one under this index.

13. Note sign typo in Equation 33.36 Morely (1955): Change trait z1 while z2 constant Want b1, b2 such that no response in trait 2 Setting b1 = 1 and solving gives weights

14. Kempthorne-Nordskog showed that br is obtained by where Gr = CG Kempthorne-Nordskog restricted index Suppose we wish the first k traits to be unchanged. Since response of an index is proportional to Gb, the constraint is CGbr =0, where C is an m X k matrix with ones on the diagonals and zeros elsewhere.

17. More generally, suppose we specify the desired gains for k combinations of traits, Solution is Tallis restriction index Here the constraint is CR = CGbr = d

18. Desired-gain indices Suppose we desire a gain of R, then since R = Gb, The desired-gains index is b = G-1R

20. Class problem Compute b for desired gains (in our soybean traits) of (1,-2,0)

21. Non-linear indices Care is required in considering the improvement goals when using a nonlinear merit function, as apparently subtle differences in the desired outcomes can become critical A related concern is whether we wish to maximize the additive Genetic value in merit in the parents or in their offspring. Again, with a linear index these are equivalent, as the mean breeding value of the parents u equals the mean value in their offspring. This is not the case with nonlinear merit.

22. Quadratic selection index

23. Linearization

24. Fitting the best linear index to a nonlinear merit function

26. Substituting the above value of b into recovers Hence, This equation describes a quadratic surface of possible, R values, Similarly, Sets of response vectors and selection differentials Given a fixed selection intensity i, we would like to know the set of possible R (response vectors) or S (selection vectors)

27. Key: Optimal weights a function of intensity of selection

28. Tandem Selection Independent Culling Selection of extremes Sequential Approaches for Multitrait selection Index selection generally optimal, but may be economic reasons for other approaches

30. Multistage selection Selecting on traits that appear in different life stages Optimal selection schemes Cotterill and James optimal 2-stage. Select 1st on x, (save p1), then then on y (save p2 = p/p1). Goal optimize the breeding value for merit g.