How to optimize and compare breeding schemes?

Transcription

1 Click to edit Master title style How to optimize and compare breeding schemes? Dr. Friedrich State Plant Breeding Institute University of Hohenheim, Germany

2 Click to edit Outline Master title style Determination of the optimum allocation of test resources How to determine the optimum allocation? Which criteria to use for optimization? Which data is required for optimization of breeding schemes?

3 Modeling the optimum allocation Click to edit Master title style needs hierarchically model framework 1. Basic level Target criterion Trait 2. Breeding level Scheme Scenario 3. Optimization level Test resources

4 Modeling the optimum allocation Click to edit Master title style needs hierarchically model framework 1. Basic level Target criterion Trait = selection gain, probability = maize grain yield 2. Breeding level Scheme Scenario = DHTC, S 1 TC-DHTC, multi-stage sel. = variance components, budget, selected fraction, technical requirements, Optimization level Test resources = number of test locations, testers, replications, DH lines

5 Determining the optimum allocation Click to edit Master title style within a given model framework 1. Basic level Selection gain Maize grain yield 2. Breeding level DHTC (I) Given scenario 3. Optimization level specific allocation of the number of testers, test locations, DH lines, replications Computation of target criterion AIM: Find the allocation maximizing selection gain for that specific def. of level 1 and 2 = optimum allocation

6 Click to edit Outline Master title style Determination of the optimum allocation of test resources How to determine the optimum allocation? Which criteria to use for optimization? Which data is required for optimization of breeding schemes?

7 Definition Click to edit of the Master target title criteria style G ih y i = selection intensity, h = square root of the heritability, σ y = standard deviation of the target variable P P gen P h P gen = prob. to have interesting genotype in population, P h = prob. to identify that genotype,

8 Selection intensity and heritability Click to edit Master title style For a fixed budget, maximization of G represents a compromise between a high number of test candidates and a high number of locations and replications Source: Becker 1993, et al. 2006

9 Click to edit Master title style Distribution of genotypes in the breeding population

10 Frequencies Selection gain G Click to edit Master title style Selection gain (ΔG) = Genotypic mean of the selected fraction α genotypic mean of the entire population μ μ' G '

11 Frequencies Probability to identify superior genotypes Click to edit Master title style P(q%) α P(q%) = Percentage exceeding the threshold, e.g., the 1- q % quantile of N(0,1) μ μ' threshold

12 Click to edit Outline Master title style Determination of the optimum allocation of test resources How to determine the optimum allocation? Which criteria to use for optimization? Which data is required for optimization of breeding schemes?

13 Economic frame and quantitativegenetic Click to edit Master title style parameters Framework: Budget for the whole breeding program Costs for genotyping, phenotyping, seed production, Ratio of variance components like: g : gy : gl : gly : e Logistics: Availability of winter nurseries Availability of marker platforms Maximum number sof field locations, lines, crosses,

14 Click Examples to edit Master for maize title style Logistic assumptions 10 DH lines can be produced from a single S1 (250 kern.) 1 multiplication of DH lines needed to have sufficient seed for perse test, isolation with tester and further multiplication Two row trials on testcross performance with 33 plants per row (sowing of 55 kernels per row) Economic assumptions Costs for producing one DH line = 8 Euro Costs for one testcross plot with two rows = 15 Euro Costs for one isolation row with 20 plants = 10 Euro Costs per hand selfing / crossing = 0.6 Euro Costs for one observation row (not harvested) = 6 Euro Equal costs in summer and winter season