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Using multi-level omics data to infer causalrelationships between correlated transcripts and
metabolites
Anita Goldinger
Diamantina InstituteUniversity of Queensland
Outline
1 Gene modulesGene modules
2 Sources of variationSources of variation
3 eQTL analysiseQTL analysis
4 MetabolomicsMetabolomics
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
Gene modules
Co-expressed modules
Aids interpretability of microarray data
Dimension reduction technique
Biology
Microarrays are prone to noise
Gene modules
11Chaussabel et al 2008 Immunity 29(1), 15´164
Modules
2
2Preininger, M., et al 2013 PLoS genetics, 9(3), e1003362.doi:10.1371/journal.pgen.1003362
Axes
3
3Preininger, M., et al 2013 PLoS genetics, 9(3), e1003362.doi:10.1371/journal.pgen.1003362
Axes
4
4Preininger, 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 position of along each of these axes can define diseasesubtypes
Causal relationships
Causal relationships
Directional statistical dependancy between variables
Integration of genomic information to elucidate regulation
Model the network of information flow from DNA tophenotype
Outline
1 Gene modulesGene modules
2 Sources of variationSources of variation
3 eQTL analysiseQTL analysis
4 MetabolomicsMetabolomics
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
Phenotypic correlation
Groups of correlated probes referred to as ”modules”
(a) Correlation matrix (b) Correlation coefficients (betweenmodule correlations highlighted)
Phenotypic covariance
Phenotypic covariance
covpxP , yPq “ covpxA, yAq ` covpxE , yE q
Genetic covariance
§ Pleiotrophy
Environmental covariance
§ Non-additive genetic effects§ Shared environmental conditions
Phenotypic correlation
Dependent on heritability estimates:
rP “ rAhxhy ` rE
b
p1´ h2xq ˚ p1´ h2y q
If estimates are similar (h2x=0.5 and h2y=0.5):
rP “ 0.5 ˚ rA ` 0.5 ˚ rE
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 (betweenmodule correlations highlighted)
Outline
1 Gene modulesGene modules
2 Sources of variationSources of variation
3 eQTL analysiseQTL analysis
4 MetabolomicsMetabolomics
eQTL
Phenotypes: Module probe expression and Axes (PC1 ofmodules)
Significance determined at FDR ą 0.05
cis region defined as 1MB from the start and end of probe
Shared eQTLs Module 2
Trans associations shared between genes in modules (% heritabilityexplained 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 modulesGene modules
2 Sources of variationSources of variation
3 eQTL analysiseQTL analysis
4 MetabolomicsMetabolomics
Results
Hexose is significantly associated with Probes of Module 3Hexose h2 = 0.47
Module Gene Metabolite Phen Corr Gen Corr p-value
3 AFF3 Hexose 0.19 0.34 4.05e-073 BLK Hexose 0.14 0.38 5.13e-053 CD19 Hexose 0.16 0.40 3.82e-063 CD72 Hexose 0.14 0.33 2.81e-053 CD79A Hexose 0.18 0.42 8.90e-083 FAM129C Hexose 0.15 0.41 1.78e-053 FCRLA Hexose 0.17 0.46 6.30e-073 VPREB3 Hexose 0.16 0.36 3.57e-06
3 Axis 3 Hexose 0.17 0.41 3.01e-07
Association Results
Shared SNPs between Modules and Metabolites
Tested significant cis and trans SNPs identified for probes inmodule 3 with Hexose
Significance determined at 0.05/n with n=17 SNPs
Metabolite SNP Effect h2 P-value
Hexose rs7082828 0.242 1.460 7.457e-04
Association Results
Module 3 shows an enrichment for rs7082828 in module 3 probes
Module Gene SNP Effect h2 P-value
3 AFF3 rs7082828 0.322 2.520 6.910e-053 BLK rs7082828 0.284 2.040 6.587e-053 CD19 rs7082828 0.335 2.820 2.668e-063 CD72 rs7082828 0.354 3.138 7.903e-073 EBF1 rs7082828 0.210 1.093 1.684e-023 FAM129C rs7082828 0.362 3.251 4.683e-073 FCRLA rs7082828 0.364 3.300 3.712e-073 POU2AF1 rs7082828 0.252 1.611 4.050e-043 VPREB3 rs7082828 0.336 2.851 2.207e-06
3 Axis 3 rs7082828 0.816 2.445 1.147e-05
Network of genomic regulation - module 3
Summary
Correlated Genes represent discrete functional units
Method to functionally annotate regulatory SNPs
Analysing multi-level omics helps to identify causalrelationships
Dissection of genetic regulation can enhance ourunderstanding of the biological processes
Acknowledgements
Acknowledgments