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

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  • 1. Using multi-level omics data to infer causal relationships between correlated transcripts and metabolites Anita Goldinger Diamantina Institute University of Queensland
  • 2. Outline 1 Gene modules Gene modules 2 Sources of variation Sources of variation 3 eQTL analysis eQTL analysis 4 Metabolomics Metabolomics
  • 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. Gene modules Co-expressed modules Aids interpretability of microarray data Dimension reduction technique Biology Microarrays are prone to noise
  • 5. Gene modules 1 1 Chaussabel et al 2008 Immunity 29(1), 15´164
  • 6. Modules 2 2 Preininger, M., et al 2013 PLoS genetics, 9(3), e1003362. doi:10.1371/journal.pgen.1003362
  • 7. Axes 3 3 Preininger, M., et al 2013 PLoS genetics, 9(3), e1003362. doi:10.1371/journal.pgen.1003362
  • 8. Axes 4 4 Preininger, M., et al 2013 PLoS genetics, 9(3), e1003362. doi:10.1371/journal.pgen.1003362
  • 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. 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. Outline 1 Gene modules Gene modules 2 Sources of variation Sources of variation 3 eQTL analysis eQTL analysis 4 Metabolomics Metabolomics
  • 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. Phenotypic correlation Groups of correlated probes referred to as ”modules” (a) Correlation matrix (b) Correlation coefficients (between module correlations highlighted)
  • 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. 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. Heritability Total SNP variance calculated using GCTA (a) Modules (b) Axes
  • 17. Genetic correlation Calculated with Bivariate REML in GCTA (a) Correlation matrix (b) Correlation coefficients (between module correlations highlighted)
  • 18. Outline 1 Gene modules Gene modules 2 Sources of variation Sources of variation 3 eQTL analysis eQTL analysis 4 Metabolomics Metabolomics
  • 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. Shared eQTLs Module 2 Trans associations shared between genes in modules (% heritability explained by eQTL listed).
  • 21. Shared eQTLs Module 5 Cis and trans associations shared between genes in modules (% heritability explained by eQTL listed).
  • 22. Shared eQTLs Module 4 Cis and trans associations shared between genes in modules (% heritability explained by eQTL listed).
  • 23. Network of genomic regulation - module 4
  • 24. Outline 1 Gene modules Gene modules 2 Sources of variation Sources of variation 3 eQTL analysis eQTL analysis 4 Metabolomics Metabolomics
  • 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. 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. 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. Network of genomic regulation - module 3
  • 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. Acknowledgements
  • 31. Acknowledgments

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