using multi-level omics data to infer causal relationships between correlated transcripts and...

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