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solGS: A Bioinformatics Solution for Genomic Selection

solGS: A Bioinformatics Solution for Genomic Selection. Isaak Y Tecle, Naama Menda, Jeremy Edwards, Lukas Mueller. Genomic Selection. Genotyped Lines. Phenotyped & Genotyped Lines. Prediction Model. Predicted Breeding Values. What solGS does…. Builds prediction models

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solGS: A Bioinformatics Solution for Genomic Selection

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  1. solGS: A Bioinformatics Solution for Genomic Selection Isaak Y Tecle, Naama Menda, Jeremy Edwards, Lukas Mueller

  2. Genomic Selection Genotyped Lines Phenotyped & Genotyped Lines Prediction Model Predicted Breeding Values

  3. What solGS does… • Builds prediction models • Predicts breeding values • Calculates selection index • Correlation analysis • Interactive data visualization

  4. Building a prediction model...3 options

  5. http://cassavabase.org/solgs

  6. Building a prediction model Option 1: Search using a trait name

  7. Estimating breeding values of a selection population Applying the model

  8. Building a prediction model Option 2: using a trial as a training population

  9. Building a prediction model Option 3: use your own list of clones

  10. Build multiple models simultaneously

  11. Estimating breeding values of a selection population for multiple traits Applying the models

  12. Calculating selection index

  13. Statistical method • Ridge regression, mixed-model • rrBLUP(Endelman , Plant Genome (2010)) • Kinship-BLUP • Marker-based realized relationship matrix • Model accuracy • Based on 10-fold cross-validation

  14. Database • Chado ND Schema • Co-developed by SGN, GDR, VectorBase and Medicago • Jung et al. Database 2011.

  15. To sum up… • Build models • Estimate breeding values • Additional tools: • correlation analysis • selection index • http://cassavabase.org/solgs • Feedback

  16. Next... • Incorporate G x E effects • Correlation analyses: phenotype values vs. breeding values, selection indices vs breeding values. • PCA and clustering • Add more prediction methods • Computational speed improvement

  17. Thanks to…

  18. Cassavabase team

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

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