Machine Learning for Biobank-Scale Genomic Data - CGSI 2022

Machine Learning for Biobank-Scale Genomic Data - CGSI 2022

Computational Genomics Summer Institute CGSI via YouTube Direct link

Gene-environment interactions (GxE)

14 of 18

14 of 18

Gene-environment interactions (GxE)

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Classroom Contents

Machine Learning for Biobank-Scale Genomic Data - CGSI 2022

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  1. 1 Intro
  2. 2 Machine learning for genomic data
  3. 3 Growth of Biobanks
  4. 4 Key inference problems
  5. 5 Genetic architecture of complex traits
  6. 6 Variance components model
  7. 7 Estimating variance components
  8. 8 Alternate estimator Method of Moments (HE-regression)
  9. 9 Randomized HE-regression (RHE) Work with a "sketch" of the genotype
  10. 10 RHE is accurate and scalable
  11. 11 Insights from applying RHE to UK Biobank
  12. 12 Dominance deviation effects
  13. 13 Dominance deviance effects
  14. 14 Gene-environment interactions (GxE)
  15. 15 Gene-gene interactions (GxG)
  16. 16 Beyond pair-wise effects
  17. 17 Random Fourier Features (RFF)
  18. 18 Missing data in Biobanks

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