Presentation of the Genomic selection web based tool SolGS at PAG 2016

1. solGS: A Web-­based Solution for
Genomic Selection
Isaak Y Tecle, Naama Menda, Guillaume
Bauchet, Lukas Mueller
Tecle et al. Bioinformatics 2014, 15:398

2. Phenotyped
&
genotyped individuals
Genomic selection…
Prediction model
Predicted
breeding
Values (GEBVs)
Genotyped selection
candidates
Training population

3. Challenges…
n Data volume, storage
n Data structuring, cleaning, imputation
n Statistical analysis complexity
n visualization and sharing

4. solGS webtool
http://cassavabase.org/solgs

5. What you can do with solGS…
n Store data
n Chado Natural Diversity schema
n Compose training populations
n Build models and predict breeding
values of selection candidates
n Test model accuracy

6. What you can do with solGS…
n Explore phenotype data, population
structure
n Check on relationship between GEBVs
vs observed phenotypes
n Calculate selection indices, correlation
n Visualize data on interactive plots

7. What is the statistical approach
behind solGS?

8. …preparing data
n Omits individuals completely missing
phenotype values
n Adjusts phenotype values for block
effects
n Averages across multiple trials after
adjusting for block effects
n Imputes missing marker data
n Median substitution

9. …statistical modeling
n Univariate
n RR-­BLUP
n Endelman, Plant Genome (2010)
n GBLUP
n Marker-­based realized relationship matrix
n Prediction accuracy
n Based on 10-­fold cross-­validation

10. How does solGS work?

12. Composing a training population:
Fitting a prediction model...
3 options

14. Fitting a prediction model…
Option 1:
Search using a trait name

26. Estimating breeding values of
selection candidates
Applying the model…

29. Fitting a prediction model…
Option 2:
Search for trials

36. Estimating breeding values of a
selection candidates for multiple
traits
Applying the models…

39. Estimating genetic correlations

41. Calculating selection indices

43. Fitting a prediction model…
Option 3:
use your own list of individuals

46. To sum up…
n Store data
n Build prediction models
n Estimate breeding values
n Additional analyses:
n Correlation analysis
n Population structure
n Selection indices
n http://cassavabase.org/solgs
n Open source code

47. Thanks to…

49. Many thanks!!
Background image: nextgencassava.org