This document describes solGS, a web-based tool for genomic selection analysis. solGS allows users to store phenotypic and genotypic data, perform pre-modeling data processing like handling missing values, build prediction models using methods like RR-BLUP and GBLUP, estimate breeding values of selection candidates, and conduct additional analyses like estimating genetic correlations and calculating selection indices. The tool aims to make genomic selection more accessible for plant breeders by providing a platform for organizing and analyzing genomic data.

1. solGS: A Web-based Genomic
Selection Analysis Tool
Isaak Y Tecle, Naama Menda, Guillaume
Bauchet, Lukas Mueller

2. Websites with solGS…

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

6. Genomic Selection advantages…
 Little or no phenotyping
 reduced cost
 Shorter breeding cycles
 Higher selection gain per unit time
 Increased prediction accuracy

7. Genomic Selection challenges…
 ‘Big data’
 Data organization, cleaning, imputation
 Data storage and accessibility
 Raw data and results visualization and
sharing
 Statistical analysis complexity

8. solGS
http://cassavabase.org/solgs

9. Data storage…
Jung et.al., 2011. Database.
Chado schema

10. Data access interfaces
Search wizard

11. pre-modeling data processing

12. Phenotype data processing…
 Missing phenotype data handling
 Adjusts phenotype means for
environmental effects
 lme4
 Combines multiple trials

13. Genotype data processing
 Filters out
 monomorphic markers
 markers with > 60% missing values
 markers with MAF < 5%
 individuals with > 80% missing values
 Imputes missing marker data
 Median substitution
 Genotype coding
 [-1, 0, 1], [0, 1, 2]

14. Prediction modeling

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

16. Use case

17. Creating a training dataset

19. Creating a custom training dataset…

23. Building a prediction model

26. Exploring model input

30. Exploring model accuracy

32. Exploring model output

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

39. Selection gain?

41. Prediction modeling for multiple
traits

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

46. Estimating genetic correlations

48. Calculating selection indices

50. To sum up…solGS
 Stores data
 Builds prediction models
 Estimates breeding values
 Additional analyses:
 Correlation analysis
 Population structure
 Selection indices
 Genetic gain
 Open source
 Organism agnostic

51. Thanks to…

53. Many thanks!!