http://bit.ly/IIBMP2020-Tanigawa =============== 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 =============== 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

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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).