November 12th, 2020
Reducing cycle time to increase rates of gain is not being fully exploited in classical programs neither the use of genomic prediction to boost even more the reduction of cycle time. Playing factors are not fully understood.
Crossing, Evaluation, Selection
\(\Delta_g = (i * \sigma_g * r)/L\)
Using genomic prediction in the recycling (selection) process to reduce the cycle time could increase the rate of genetic gain.
|TPn_PPn_SPf4f5||TrainingPop=NULL,PredictedPop=NULL,RecyclingPop=F4-F5 using an index.|
|TPn_PPn_SPf5f6||TrainingPop=NULL,PredictedPop=NULL,RecyclingPop=F5-F6 using an index.|
|TPn_PPn_SPf6f7||TrainingPop=NULL,PredictedPop=NULL,RecyclingPop=F6-F7 using an index.|
|TPn_PPn_SPf7f8||TrainingPop=NULL,PredictedPop=NULL,RecyclingPop=F7-F8 using an index.|
|TPn_PPn_SPf7f8_NF||TrainingPop=NULL,PredictedPop=NULL,RecyclingPop=F7-F8 using an index with no family selection.|
|TPf5f6_PPf1_SPf1||TrainingPop=F5-F6, PredictedPop=F1, RecyclingPop=F1 using an index.|
A 20 year burn-in period was used. Burn-in was followed by a 20 year evaluation period to measure rates of genetic gain in F9 lines. Genotype-by-year, genotype-by-location interaction variances were assumed to be equivalent to main genetic variance. 25 replications done. We simulated 5 complex and 3 simple traits to be behind the genetic merit. TP=random, N.TP=3K, N.Markers=5K.