2. Purposes
Gain understanding of genomic
selection, GS model building, breeding
values prediction, assessing model data
input and output quality.
Brainstorm for ideas to make the tool
suit better your research purposes.
5. GS advantages
Little or no phenotyping
reduced cost
Shorter breeding cycles
Higher selection gain per unit time
Increased prediction accuracy
9. What you can do with solGS…
Store data
Chado Natural Diversity schema
Create training dataset
Build models and predict breeding
values of selection candidates
Test model accuracy
10. What you can do with solGS…
Explore phenotype data
Evaluate population structure
Check on relationship between
GEBVs vs observed phenotypes
Calculate selection indices, correlation
Visualize data on interactive plots
Calculate selection response
11. What is the statistical approach
behind solGS?
12. …preparing phenotype data
Omits individuals completely missing
phenotype values
Adjusts phenotype values for block
effects
Averages across multiple trials after
adjusting for block effects
13. …preparing genotype data
Removes out monomorphic markers
Removes markers with > 60% missing
values
Removes markers with MAF < 5%
Removes individuals with > 80%
missing values
Imputes missing marker data
Median substitution
18. Demo: Part I
Create training data set & build model
Explore model input and output
Phenotype and genetic correlation
Population structure
Selection index
20. Things to consider…Phenotype data
Number of phenotyped individuals
Minimum 20 clones
Relevant to target environment
Data quality
Experimental design
Measurement accuracy
Missing values
outliers
21. Things to consider…genotype data
Marker number, genome distribution,
polymorphism,
Data quality
Allele calling accuracy
Missing values (Per marker, individual)
Minor alleles
Heterozygosity,
LD
Population structure
23. single trial – single trait
Create training data set and build
model
Trial method
Search for trial ‘Cassava Ibadan 2002/03’
Create a training dataset with that trial
Description, correlation
Build a model for FRW
Explore model input and output,
model accuracy
Download GEBVs
24. Exercise: single trial – single trait
Create training data set and build
model
Search for your trial
Create a training dataset with that trial
Check description, correlation
Build a model for your trait
Explore model input and output,
Population structure
model accuracy
Download GEBVs
25. single trial – multiple traits
Create training data set and build
models
Search for trial ‘Cassava Ibadan 2002/03’
Create a training dataset with that trial
Description, correlation
Build models for FRW and CMDS
Explore model input and output for each model,
Genetic correlation
Selection index
26. Exercise: single trial – multiple traits
Create training data set and build
models
Search for your trial
Create a training dataset with that trial
Check description, correlation
Build models for two traits at the same time
Explore model input and output for each model,
Genetic correlation
Calculate and download selection index
27. Combined trials – single trait
Create training data set and build
models using two trials
Search for ‘cassava ibadan 02/03 & 01/02’
Create a training dataset with the trials
Check description, correlation
Build a model for FRW
Explore model input and output for the model,
Population structure
Prediction accuracy
Download GEBV
28. Exercise: combined trials – single trait
Create training data set and build
models using two trials
Search for your trials
Create a training dataset with the trials
Check description, correlation
Build a model for your trait
Explore model input and output for the model,
Population structure
Prediction accuracy
Download GEBV
29. Using list – single trait
Create training data set and build a
model using plots list
Using the search wizard create a plots list from
trial ‘cassava ibadan 2002/03 plots’
Create a training dataset with the list
Check description, correlation
Build a model for your FRW
Explore model input and output for the model,
Population structure
Prediction accuracy
Download GEBV
30. Exercise: Using list – single trait
Create training data set and build a
model using plots list
Using the search wizard create a plots list from
a trial… select all plots..
Create a training dataset with the list
Check description, correlation
Build a model for your trait
Explore model input and output for the model,
Population structure
Prediction accuracy
Download GEBV
31. Demo: Part II
Predict breeding values of selection
populations
Genetic correlation
Selection index
Selection gain
32. Things to consider when applying a
model to predict breeding values of
selection populations
33. Things to consider…applying the model
Training population vs selection
population genetic relationship
Target environment
Marker types used
Population structure
34. Predict GEBVs of a Selection population
Create training data set & build model
Cassava Ibadan 2002/03
FRW
Search for a selection population
Cassava Ibadan 2003/04
Predict GEBVs for the selection
population
Check selection response
Download GEBVs
35. Exercise: Selection Population Prediction
Create training data set & build model
use one of the models you already built
Search for a selection population
Related to the training population
Predict GEBVs for the selection
population
Check selection response
Download GEBVs
36. Multiple Traits: Predict GEBVs of a Selection population
Create training data set & build model
Cassava Ibadan 2002/03
FRW, CMDS
Search for a selection population
Cassava Ibadan 2003/04
Predict GEBVs for both traits for the
selection population
Check selection response
Download GEBVs
37. Exercise: Multiple Traits selection population
prediction
Create training data set & build model
Use previous two models from your training
populations
Search for a selection population
Predict GEBVs for both traits for the
selection population
Check genetic correlation
Calculate selection index
38. List: Predict GEBVs of a Selection population
Create training data set & build model
Cassava Ibadan 2002/03
FRW
Search for a selection candidates list
Cassava Ibadan 213 genotypes
Predict GEBVs for the selection
population
Check selection response
Download GEBVs
39. Exercise: selection candidates list
Create training data set & build model
Go to a previous model page
Create a selection candidates list
Use search wizard to create accessions list
Using the model predict GEBVs of the
list
Check selection response
Download GEBVs
40. Demo: Part III
Trait search
Search for ‘fresh root weight’
Select trial ‘cassava ibadan 2002/03’
Check model output
80. To sum up…
Store data
Build prediction models
Estimate breeding values
Additional analyses:
Correlation analysis
Population structure
Selection indices
http://cassavabase.org/solgs
Open source code