September 9th, 2020

The GS Baseline

The scope of the simulations to come

1. Introduction to the problem

Crop by region

IITA-Cassava NextGen

Problem specification

Accuracy could be sub-optimal thereby reducing the rate of genetic gain.

Breeding strategy component tackled

Crossing, Evaluation, Selection

Breeders’ equation terms tackled


\(\Delta_g = (i * \sigma_g * r)/L\)


Using genetic information from a genetic relationship matrix (GBLUP) or pedigree information from a pedigree relationship matrix (PBLUP) will increase the rate of genetic gain over the normal BLUP

2. Materials and methods


Treatment Description
BLUP_PYT-AYT The best conventional scheme without markers (PYT+AYT)
PBLUP_PYT-AYT Considering pedigree information in addition to the best conventional scheme (PYT+AYT)
GBLUP_PYT-AYT Considering genetic information in addition to the best conventional scheme (PYT+AYT)
GBLUP_CE Considering genetic information at an early stage (CE) in additional to the best conventional scheme (PYT+AYT)

Simulation procedure

A 20 year burn-in period was modeled using the baseline. The burn-in was followed by a 20 year evaluation period to measure rates of genetic gain for all treatments. Genetic gain was measured by assessing changes in genetic merit at F1. Genotype-by-year interaction variance was assumed to be equivalent to genetic variance (based on average correlation between locations being equal to 0.5). 30 replications done. We considered a multi-trait reality using an index provided by breeders with traitsNames = “DM”, “HI”, “FYLD”, “MCMDS”, “PLTHT”, “RTSZ” and econWt = c(20, 7, 20, -10, 10, 10).

The genetic model: General

Simple trait & Complex trait to illustrate the relationship between h2, accuracy and family size for the 3 models (BLUP, PBLUP, GBLUP)

At certain accuracy there doesn’t seem to be more gain from GBLUP or PBLUP

3.0 The best conventional scheme

A mixed crossing block between PYT and AYT is optimal for conventional scheme

3.1 Results in terms if genetic gain

More returns on GBLUP for accuracy if applied in early stages (CE) where accuracy is very low

3.2 Results in terms of accuracy (yield)

comparing models with accuracy at CE, PYT and AYT

There is a comparative advantage of using GBLUP at early stages where accuracy is very low.

4. Conclusion

Adding pedigree information did not have any advantage on accuracy compared to normal BLUP.

Using GBLUP as a surrogate of genetic value for selection can increase the accuracy dramatically at low h2 even for small family sizes.

Using GBLUP at recycling stages with already higher accuracy increases the accuracy but the magnitude does not justify the additional investment, as it does not change selection decisions very much.

The use of GBLUP at early stages e.g. CE where the nReps and nLocs is very low is the best approach to apply GBLUP for accuracy purposes.

We recommend to use GBLUP to keep accuracy high and to make it the first step to start using GS.