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Multi-trait analysis informs genetic disease studies (IIBMP 2020)

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http://bit.ly/IIBMP2020-Tanigawa
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2020年日本バイオインフォマティクス学会年会・第9回生命医薬情報学連合学会で発表の機会をいただきました。スライドを公開します。こちらのページもあわせてご覧ください:http://bit.ly/IIBMP2020-Tanigawa
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I had a wonderful opportunity to give a virtual oral presentation at Informatics in Biology, Medicine, and Pharmacology conference, 2020. I talked about joint analysis of multiple traits in genetic disease studies using DeGAs and multi-PRS as example projects. Please check this website for more information: http://bit.ly/IIBMP2020-Tanigawa

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Multi-trait analysis informs genetic disease studies (IIBMP 2020)

  1. 1. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Multi-trait analysis informs genetic disease studies Yosuke Tanigawa & Manuel A. Rivas Stanford University 2020/9/1 Informatics in Biology, Medicine, and Pharmacology conference 2020 http://bit.ly/IIBMP2020-Tanigawa 1 @yk_tani @manuelrivascruz
  2. 2. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Genetics: genome-phenome mapping • Inference ▪ Identification of risk and protective alleles • Prediction ▪ Genetic prediction of diseases ? 2
  3. 3. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Genetics: genome-phenome mapping • Inference ▪ Identification of risk and protective alleles • Prediction ▪ Genetic prediction of diseases ? 3 Polygenic risk score, ... GWAS, PheWAS, ...
  4. 4. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Massive data in population biobanks provide opportunities 4C. Bycroft et al., Nature. 562, 203–209 (2018). • UK Biobank: prospective cohort study • 500k individuals • Genetics ▪ Genotyping array ▪ Imputation ▪ Exome • Dense phenotype ▪ Assay and laboratory tests • Serum and Urine tests, ... ▪ Disease outcomes ▪ Cancer registry ▪ Medication/Drug ▪ Imaging (brain, liver, eye, …) ▪ Questionnaire
  5. 5. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Massive data in population biobanks provide opportunities 5C. Bycroft et al., Nature. 562, 203–209 (2018). • UK Biobank: prospective cohort study • 500k individuals • Genetics ▪ Genotyping array ▪ Imputation ▪ Exome • Dense phenotype ▪ Assay and laboratory tests • Serum and Urine tests, ... ▪ Disease outcomes ▪ Cancer registry ▪ Medication/Drug ▪ Imaging (brain, liver, eye, …) ▪ Questionnaire multi-trait analysis in densely-phenotyped cohorts
  6. 6. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Multi-trait analysis -- 2 case studies 6 1) DeGAs (Decomposition of Genetic Associations) 2) Multi-PRS (Polygenic risk score) applied to 35 biomarkers Inference Prediction
  7. 7. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Multi-trait analysis -- 2 case studies 7 1) DeGAs (Decomposition of Genetic Associations) 2) Multi-PRS (Polygenic risk score) applied to 35 biomarkers Inference Prediction
  8. 8. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu GWAS of a complex trait finds many associations Global Biobank Engine (gbe.stanford.edu) 8
  9. 9. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu PheWAS finds pleiotropic effects of a variant 9 Tanigawa*, Li*, et al. Nat Comm (2019).
  10. 10. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Extreme polygenicity and pervasive pleiotropy limits interpretability of associations Genetic variants Complex traits 10
  11. 11. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu DeGAs latent components entangle many-to-many mapping Genetic variants Complex traitsLatent components 11
  12. 12. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Let’s use latent components of genome- and phenome-wide associations Decomposition of Genetic Associations (DeGAs) Let’s paint genetics of diseases! Edgar Degas “Dancer Taking a Bow (The Star)” ca. 1878 12 Key idea
  13. 13. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Low-rank representation of association summary statistics provides latent components 1. Genome & phenome-wide association summary statistic matrix 2. Truncated-singular value decomposition (TSVD) 3. Decompose the genetics association Summary statistics from association analysis (beta or log odds ratio) 13 Tanigawa*, Li*, et al. Nat Comm (2019).
  14. 14. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Biplot annotation helps interpretation of DeGAs latent components 14 Tanigawa*, Li*, et al. Nat Comm (2019).
  15. 15. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Biplot annotation helps interpretation of DeGAs latent components 15 Tanigawa*, Li*, et al. Nat Comm (2019).
  16. 16. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Biplot annotation helps interpretation of DeGAs latent components 16 Tanigawa*, Li*, et al. Nat Comm (2019).
  17. 17. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Biplot annotation helps interpretation of DeGAs latent components 17 Tanigawa*, Li*, et al. Nat Comm (2019).
  18. 18. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Biplot annotation helps interpretation of DeGAs latent components 18 Tanigawa*, Li*, et al. Nat Comm (2019).
  19. 19. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu DeGAs decomposes polygenic GWAS signals “Fat” component “Fat-free” component 19 Tanigawa*, Li*, et al. Nat Comm (2019).
  20. 20. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu DeGAs applied to protein-truncating variants (PTVs) highlights potential therapeutic target 20 Tanigawa*, Li*, et al. Nat Comm (2019).
  21. 21. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu DeGAs applied to protein-truncating variants (PTVs) highlights potential therapeutic target 21 Tanigawa*, Li*, et al. Nat Comm (2019). Jihan Li
  22. 22. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu 1) DeGAs (Decomposition of Genetic Associations) 2) Multi-PRS (Polygenic risk score) applied to 35 biomarkers Multi-trait analysis -- 2 case studies 22 Inference Prediction
  23. 23. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Polygenic prediction Polygenic risk score (PRS) i-th individual j-th variant G: genotype β: effect size 23 Qian, et al. bioRxiv (2019). ?
  24. 24. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Polygenic prediction Polygenic risk score (PRS) i-th individual j-th variant G: genotype β: effect size 24 GWAS/Univariate model - Direct interpretation - Convenient computation - Weak prediction Multivariate model - Less interpretable - High computational cost - Better prediction Qian, et al. bioRxiv (2019). Junyang Qian L1 penalized regression w/ Lasso BASIL algorithm & R snpnet package
  25. 25. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Genetics of 35 Serum and Urine Biomarkers 25 Coding variants with absolute value of effect size > 0.1 SD per allele Sinnott-Armstrong*, Tanigawa*, et al. bioRxiv (2019).
  26. 26. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Genetics of 35 Serum and Urine Biomarkers 26 Coding variants with absolute value of effect size > 0.1 SD per allele Sinnott-Armstrong*, Tanigawa*, et al. bioRxiv (2019). Polygenic risk scores (PRSs) for 35 biomarkers
  27. 27. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Biomarkers are more heritable than diseases 27 Sinnott-Armstrong*, Tanigawa*, et al. bioRxiv (2019). Nasa Sinnott-Armstrong
  28. 28. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Is the same true of polygenic risk scores? 28 Sinnott-Armstrong*, Tanigawa*, et al. bioRxiv (2019).
  29. 29. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Is the same true of polygenic risk scores? 29 Sinnott-Armstrong*, Tanigawa*, et al. bioRxiv (2019). multi-PRS := weighted sum of PRSs
  30. 30. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu multi-PRS improves prediction of prevalent kidney failure 30 Sinnott-Armstrong*, Tanigawa*, et al. bioRxiv (2019). multi-PRS := weighted sum of PRSs
  31. 31. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu multi-PRS improves prediction of diseases 31 Sinnott-Armstrong*, Tanigawa*, et al. bioRxiv (2019).
  32. 32. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Summary: Multi-trait analysis informs genetic disease studies - [Inference] DeGAs (Decomposition of Genetic Associations) - New method to map latent genetic components - Therapeutic target identification - [Prediction] multi-PRS - Biomarker PRS improves disease prediction 32Tanigawa*, Li*, et al. Nat Comm (2019). Sinnott-Armstrong*, Tanigawa*, et al. bioRxiv (2019).
  33. 33. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Acknowledgements • Manuel A. Rivas • Nasa Sinnott-Armstrong • Jiehan Li • Erik Ingelsson • Robert Tibshirani • Trevor Hastie • Junyang Qian • Jonathan K. Pritchard • Gill Bejerano • Matthew Aguirre • Guhan Ram Venkataraman • David Amar • Nina J. Mars • Christian Benner 33 and more!
  34. 34. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Reference Today’s slides: http://bit.ly/IIBMP2020-Tanigawa Questions & comments: @yk_tani or email 1. [DeGAs] Tanigawa*, J. Li*, J. M. Justesen, H. Horn, M. Aguirre, C. DeBoever, C. Chang, B. Narasimhan, K. Lage, T. Hastie, C. Y. Park, G. Bejerano, E. Ingelsson, M. A. Rivas, Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology. Nat Commun. 10, 1-14 (2019). https://doi.org/10.1038/s41467-019-11953-9 2. [multi-PRS / Biomarkers] N. Sinnott-Armstrong*, Y. Tanigawa*, D. Amar, N. J. Mars, M. Aguirre, G. R. Venkataraman, M. Wainberg, H. M. Ollila, J. P. Pirruccello, J. Qian, A. Shcherbina, FinnGen, F. Rodriguez, T. L. Assimes, V. Agarwala, R. Tibshirani, T. Hastie, S. Ripatti, J. K. Pritchard, M. J. Daly, M. A. Rivas, Genetics of 38 blood and urine biomarkers in the UK Biobank. bioRxiv, 660506 (2019). https://doi.org/10.1101/660506 3. [Polygenic risk score with BASIL/snpnet] J. Qian, Y. Tanigawa, W. Du, M. Aguirre, R. Tibshirani, M. A. Rivas, T. Hastie, A Fast and Scalable Framework for Large-scale and Ultrahigh-dimensional Sparse Regression with Application to the UK Biobank. bioRxiv, 630079 (2019). https://doi.org/10.1101/630079 4. Global Biobank Engine: http://gbe.stanford.edu/ 34
  35. 35. Slides: http://bit.ly/IIBMP2020-Tanigawa http://rivaslab.stanford.edu Summary: Multi-trait analysis informs genetic disease studies Today’s slides: http://bit.ly/IIBMP2020-Tanigawa Questions & comments: @yk_tani or email - [Inference] DeGAs (Decomposition of Genetic Associations) - New method to map latent genetic components - Therapeutic target identification - [Prediction] multi-PRS - Biomarker PRS improves disease prediction 35Tanigawa*, Li*, et al. Nat Comm (2019). Sinnott-Armstrong*, Tanigawa*, et al. bioRxiv (2019).

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