Bringing a selection index into the CIMMYT-Maize

Transcript:Bringing a selection index into the CIMMYT-Maize:
Bringing a selection index into the CIMMYT-Maize programs Giovanny Eduardo Covarrubias-Pazaran Breeding scheme optimization lead Approach What we know about the selection method in genetic gain Understand the selection procedure in an algorithm fashion. Identify which parts of the selection procedure can be replaced with an index. Build an index. Compare to current approach using selection differentials and total gain. Refine an index. Trait.1 performance What we know about the effect of the selection method in the genetic gain? Do simulations show that picking transgressive individuals is a good method to increase genetic gains? T1: use an index to pick the best for total merit (10%) T2: pick the best for yield and then best for zinc (31% > 31% = 10%) T3: pick the best individuals for each trait (top 5% in each = 10%) Trait.2 performance Trait.1 performance Trait.2 performance Trait.1 performance Trait.2 performance Independent culling Selection index Tandem-type selection y=(t1*w1)+(t2*w2) t1 > x t2 > z t1 > x t2 > z The best parents are not the extreme value individuals with lack of performance in other traits Trait.1 performance Trait.2 performance Trait.1 performance Trait.2 performance Independent culling Selection index Trait.1 performance Trait.2 performance Tandem-type selection Trait 1 Trait 2 Total merit 70% 50% 66% 38% 57% 53% y=(t1*w1)+(t2*w2) t1 > x t2 > z t1 > x t2 > z Selection step S3 to S4 using an index vs current method Understand the selection procedure in an algorithm fashion S3 to S4 We identified that the process can be mapped back to a set of reduction and selection steps, each consisting in trait conditions (value and directionality): As many traits as needed involved in each step Looking at the relation of some of the important traits If the selection differentials represent the breeder’s goal, then the weights determines the merit of individuals selected. Weights can be back calculated weight(b) Opt. 1.4254025 MD 0.0717673 LN 0.1703238 Anthesis.Date 0.1205678 [,1] [,2] [,3] [,4] [1,] 0.74 0.23 0.30 -0.52 [2,] 0.23 0.69 0.33 -1.13 [3,] 0.30 0.33 0.42 -0.71 [4,] -0.52 -1.13 -0.71 7.87 [,1] [1,] 1.09 [2,] 0.35 [3,] 0.48 [4,] -0.39 = _ Current selection differentials with Yoseph’s patented approach vs index A comparison of selections shows some differences Recycling step using an index vs current method r=0.11 r=0.82 r=0.7 r=0.09 weight (b) GCA_GY.opt 3.15843983 GCA_AD.opt -0.09933895 GCA_ASI.opt -0.36068438 GCA_EAR.ASP.opt -0.53326231 GCA_BHC.opt 0.01720044 GCA_ER.opt 0.04462010 GCA_EPP.opt -2.46773028