Using multi-level omics data to infer causal
relationships between correlated transcripts and
metabolites
Anita Goldinger
...
Outline

1 Gene modules

Gene modules
2 Sources of variation

Sources of variation
3 eQTL analysis

eQTL analysis
4 Metabo...
Gene modules

Gene co-expression
Gene products function together in complex networks
Identified with clustering algorithms
...
Gene modules

Co-expressed modules
Aids interpretability of microarray data
Dimension reduction technique
Biology
Microarr...
Gene modules

1
1

Chaussabel et al 2008 Immunity 29(1), 15´164
Modules

2
2

Preininger, M., et al 2013 PLoS genetics, 9(3), e1003362.
doi:10.1371/journal.pgen.1003362
Axes

3
3

Preininger, M., et al 2013 PLoS genetics, 9(3), e1003362.
doi:10.1371/journal.pgen.1003362
Axes

4

4

Preininger, M., et al 2013 PLoS genetics, 9(3), e1003362.
doi:10.1371/journal.pgen.1003362
Axes

Gene expression is constrained amongst these axes
Environmental influences causes changes in specific axes
The positio...
Causal relationships

Causal relationships
Directional statistical dependancy between variables
Integration of genomic inf...
Outline

1 Gene modules

Gene modules
2 Sources of variation

Sources of variation
3 eQTL analysis

eQTL analysis
4 Metabo...
Brisbane Systems Genetics Study (BSGS)

862 individuals
314 families
Complex pedigree structure
§ Parent-offsprint
§ Siblin...
Phenotypic correlation
Groups of correlated probes referred to as ”modules”

(a) Correlation matrix

(b) Correlation coeffic...
Phenotypic covariance

Phenotypic covariance
cov pxP , yP q “ cov pxA , yA q ` cov pxE , yE q
Genetic covariance
§ Pleiotr...
Phenotypic correlation

Dependent on heritability estimates:
b
2
2
rP “ rA hx hy ` rE p1 ´ hx q ˚ p1 ´ hy q
2
2
If estimat...
Heritability
Total SNP variance calculated using GCTA

(a) Modules

(b) Axes
Genetic correlation
Calculated with Bivariate REML in GCTA

(a) Correlation matrix

(b) Correlation coefficients (between
mo...
Outline

1 Gene modules

Gene modules
2 Sources of variation

Sources of variation
3 eQTL analysis

eQTL analysis
4 Metabo...
eQTL

Phenotypes: Module probe expression and Axes (PC1 of
modules)
Significance determined at FDR ą 0.05
cis region defined...
Shared eQTLs Module 2
Trans associations shared between genes in modules (% heritability
explained by eQTL listed).
Shared eQTLs Module 5
Cis and trans associations shared between genes in modules (%
heritability explained by eQTL listed)...
Shared eQTLs Module 4
Cis and trans associations shared between genes in modules (%
heritability explained by eQTL listed)...
Network of genomic regulation - module 4
Outline

1 Gene modules

Gene modules
2 Sources of variation

Sources of variation
3 eQTL analysis

eQTL analysis
4 Metabo...
Results

Hexose is significantly associated with Probes of Module 3
Hexose h2 = 0.47
Module
3
3
3
3
3
3
3
3
3

Gene
AFF3
BL...
Association Results

Shared SNPs between Modules and Metabolites
Tested significant cis and trans SNPs identified for probes...
Association Results
Module 3 shows an enrichment for rs7082828 in module 3 probes
Module
3
3
3
3
3
3
3
3
3
3

Gene
AFF3
BL...
Network of genomic regulation - module 3
Summary

Correlated Genes represent discrete functional units
Method to functionally annotate regulatory SNPs
Analysing mu...
Acknowledgements
Acknowledgments
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Using multi-level omics data to infer causal relationships between correlated transcripts and metabolites - Anita Goldinger

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Using multi-level omics data to infer causal relationships between correlated transcripts and metabolites - Anita Goldinger

  1. 1. Using multi-level omics data to infer causal relationships between correlated transcripts and metabolites Anita Goldinger Diamantina Institute University of Queensland
  2. 2. Outline 1 Gene modules Gene modules 2 Sources of variation Sources of variation 3 eQTL analysis eQTL analysis 4 Metabolomics Metabolomics
  3. 3. Gene modules Gene co-expression Gene products function together in complex networks Identified with clustering algorithms Genetic co-regulation Functional pathways Give a greater understanding of biological networks
  4. 4. Gene modules Co-expressed modules Aids interpretability of microarray data Dimension reduction technique Biology Microarrays are prone to noise
  5. 5. Gene modules 1 1 Chaussabel et al 2008 Immunity 29(1), 15´164
  6. 6. Modules 2 2 Preininger, M., et al 2013 PLoS genetics, 9(3), e1003362. doi:10.1371/journal.pgen.1003362
  7. 7. Axes 3 3 Preininger, M., et al 2013 PLoS genetics, 9(3), e1003362. doi:10.1371/journal.pgen.1003362
  8. 8. Axes 4 4 Preininger, M., et al 2013 PLoS genetics, 9(3), e1003362. doi:10.1371/journal.pgen.1003362
  9. 9. Axes Gene expression is constrained amongst these axes Environmental influences causes changes in specific axes The position of along each of these axes can define disease subtypes
  10. 10. Causal relationships Causal relationships Directional statistical dependancy between variables Integration of genomic information to elucidate regulation Model the network of information flow from DNA to phenotype
  11. 11. Outline 1 Gene modules Gene modules 2 Sources of variation Sources of variation 3 eQTL analysis eQTL analysis 4 Metabolomics Metabolomics
  12. 12. Brisbane Systems Genetics Study (BSGS) 862 individuals 314 families Complex pedigree structure § Parent-offsprint § Siblings § MZ and DZ twins Multi-omic data § SNP genotype § Gene expression § Metabolomic
  13. 13. Phenotypic correlation Groups of correlated probes referred to as ”modules” (a) Correlation matrix (b) Correlation coefficients (between module correlations highlighted)
  14. 14. Phenotypic covariance Phenotypic covariance cov pxP , yP q “ cov pxA , yA q ` cov pxE , yE q Genetic covariance § Pleiotrophy Environmental covariance § Non-additive genetic effects § Shared environmental conditions
  15. 15. Phenotypic correlation Dependent on heritability estimates: b 2 2 rP “ rA hx hy ` rE p1 ´ hx q ˚ p1 ´ hy q 2 2 If estimates are similar (hx =0.5 and hy =0.5): rP “ 0.5 ˚ rA ` 0.5 ˚ rE
  16. 16. Heritability Total SNP variance calculated using GCTA (a) Modules (b) Axes
  17. 17. Genetic correlation Calculated with Bivariate REML in GCTA (a) Correlation matrix (b) Correlation coefficients (between module correlations highlighted)
  18. 18. Outline 1 Gene modules Gene modules 2 Sources of variation Sources of variation 3 eQTL analysis eQTL analysis 4 Metabolomics Metabolomics
  19. 19. eQTL Phenotypes: Module probe expression and Axes (PC1 of modules) Significance determined at FDR ą 0.05 cis region defined as 1MB from the start and end of probe
  20. 20. Shared eQTLs Module 2 Trans associations shared between genes in modules (% heritability explained by eQTL listed).
  21. 21. Shared eQTLs Module 5 Cis and trans associations shared between genes in modules (% heritability explained by eQTL listed).
  22. 22. Shared eQTLs Module 4 Cis and trans associations shared between genes in modules (% heritability explained by eQTL listed).
  23. 23. Network of genomic regulation - module 4
  24. 24. Outline 1 Gene modules Gene modules 2 Sources of variation Sources of variation 3 eQTL analysis eQTL analysis 4 Metabolomics Metabolomics
  25. 25. Results Hexose is significantly associated with Probes of Module 3 Hexose h2 = 0.47 Module 3 3 3 3 3 3 3 3 3 Gene AFF3 BLK CD19 CD72 CD79A FAM129C FCRLA VPREB3 Axis 3 Metabolite Hexose Hexose Hexose Hexose Hexose Hexose Hexose Hexose Hexose Phen Corr 0.19 0.14 0.16 0.14 0.18 0.15 0.17 0.16 0.17 Gen Corr 0.34 0.38 0.40 0.33 0.42 0.41 0.46 0.36 0.41 p-value 4.05e-07 5.13e-05 3.82e-06 2.81e-05 8.90e-08 1.78e-05 6.30e-07 3.57e-06 3.01e-07
  26. 26. Association Results Shared SNPs between Modules and Metabolites Tested significant cis and trans SNPs identified for probes in module 3 with Hexose Significance determined at 0.05/n with n=17 SNPs Metabolite Hexose SNP rs7082828 Effect 0.242 h2 1.460 P-value 7.457e-04
  27. 27. Association Results Module 3 shows an enrichment for rs7082828 in module 3 probes Module 3 3 3 3 3 3 3 3 3 3 Gene AFF3 BLK CD19 CD72 EBF1 FAM129C FCRLA POU2AF1 VPREB3 Axis 3 SNP rs7082828 rs7082828 rs7082828 rs7082828 rs7082828 rs7082828 rs7082828 rs7082828 rs7082828 rs7082828 Effect 0.322 0.284 0.335 0.354 0.210 0.362 0.364 0.252 0.336 0.816 h2 2.520 2.040 2.820 3.138 1.093 3.251 3.300 1.611 2.851 2.445 P-value 6.910e-05 6.587e-05 2.668e-06 7.903e-07 1.684e-02 4.683e-07 3.712e-07 4.050e-04 2.207e-06 1.147e-05
  28. 28. Network of genomic regulation - module 3
  29. 29. Summary Correlated Genes represent discrete functional units Method to functionally annotate regulatory SNPs Analysing multi-level omics helps to identify causal relationships Dissection of genetic regulation can enhance our understanding of the biological processes
  30. 30. Acknowledgements
  31. 31. Acknowledgments

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