using multi-level omics data to infer causal relationships between correlated transcripts and...
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Using multi-level omics data to infer causalrelationships between correlated transcripts and
metabolites
Anita Goldinger
Diamantina InstituteUniversity of Queensland
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Outline
1 Gene modulesGene modules
2 Sources of variationSources of variation
3 eQTL analysiseQTL analysis
4 MetabolomicsMetabolomics
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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
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Gene modules
Co-expressed modules
Aids interpretability of microarray data
Dimension reduction technique
Biology
Microarrays are prone to noise
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Gene modules
11Chaussabel et al 2008 Immunity 29(1), 15´164
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Modules
2
2Preininger, M., et al 2013 PLoS genetics, 9(3), e1003362.doi:10.1371/journal.pgen.1003362
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Axes
3
3Preininger, M., et al 2013 PLoS genetics, 9(3), e1003362.doi:10.1371/journal.pgen.1003362
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Axes
4
4Preininger, M., et al 2013 PLoS genetics, 9(3), e1003362.doi:10.1371/journal.pgen.1003362
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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
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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
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Outline
1 Gene modulesGene modules
2 Sources of variationSources of variation
3 eQTL analysiseQTL analysis
4 MetabolomicsMetabolomics
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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
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Phenotypic correlation
Groups of correlated probes referred to as ”modules”
(a) Correlation matrix (b) Correlation coefficients (betweenmodule correlations highlighted)
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Phenotypic covariance
Phenotypic covariance
covpxP , yPq “ covpxA, yAq ` covpxE , yE q
Genetic covariance
§ Pleiotrophy
Environmental covariance
§ Non-additive genetic effects§ Shared environmental conditions
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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
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Heritability
Total SNP variance calculated using GCTA
(a) Modules (b) Axes
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Genetic correlation
Calculated with Bivariate REML in GCTA
(a) Correlation matrix (b) Correlation coefficients (betweenmodule correlations highlighted)
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Outline
1 Gene modulesGene modules
2 Sources of variationSources of variation
3 eQTL analysiseQTL analysis
4 MetabolomicsMetabolomics
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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
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Shared eQTLs Module 2
Trans associations shared between genes in modules (% heritabilityexplained by eQTL listed).
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Shared eQTLs Module 5
Cis and trans associations shared between genes in modules (%heritability explained by eQTL listed).
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Shared eQTLs Module 4
Cis and trans associations shared between genes in modules (%heritability explained by eQTL listed).
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Network of genomic regulation - module 4
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Outline
1 Gene modulesGene modules
2 Sources of variationSources of variation
3 eQTL analysiseQTL analysis
4 MetabolomicsMetabolomics
![Page 25: Using multi-level omics data to infer causal relationships between correlated transcripts and metabolites - Anita Goldinger](https://reader033.vdocuments.site/reader033/viewer/2022060119/559030481a28abe9508b4714/html5/thumbnails/25.jpg)
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
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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
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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
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Network of genomic regulation - module 3
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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
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Acknowledgements
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Acknowledgments