Omics Integration
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The document describes a study that integrated genetic, epigenetic, and transcriptomic data from a large cohort to identify quantitative trait loci (QTLs) and causal relationships between molecular phenotypes. The study identified eQTLs, meQTLs, and aceQTLs and found 240 loci associated with all three data types. Bayesian networks were constructed for each multi-QTL to model potential causal relationships, though most networks showed independence between phenotypes. Future work involves expanding the analysis and better modeling causal effects.
Omics Integration
- 1. Integrative causality analysis of genetic, epigenetic, and transcriptomic data in a large cohort Rosemary McCloskey and Sara Mostafavi rmcclosk.math@gmail.com http://slideshare.net/rmcclosk/omics-integration March 27, 2015 R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 1 / 12
- 2. Motivation genetic, epigenetic, and transcriptomic data provide snapshots of cellular processes GATTACA gene expression methylation histone acetylation genotype R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 2 / 12
- 3. Motivation genetic, epigenetic, and transcriptomic data provide snapshots of cellular processes usually one data type is studied at a time, in relation to a phenotype or disease GATTACA gene expression methylation histone acetylation genotype R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 2 / 12
- 4. Motivation genetic, epigenetic, and transcriptomic data provide snapshots of cellular processes usually one data type is studied at a time, in relation to a phenotype or disease GATTACA ? gene expression methylation histone acetylation genotype how do these data fit together? R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 2 / 12
- 5. The data large cohort designed to study cognitive decline and Alzheimer’s disease 2 19 1080 0 3 392 152 20 0 1 40 61 47 17 11 expression methylation acetylation genotype R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 3 / 12
- 6. The data large cohort designed to study cognitive decline and Alzheimer’s disease genotype, gene expression, DNA methylation, and histone acetylation (CHiP-seq) data 2 19 1080 0 3 392 152 20 0 1 40 61 47 17 11 expression methylation acetylation genotype R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 3 / 12
- 7. The data large cohort designed to study cognitive decline and Alzheimer’s disease genotype, gene expression, DNA methylation, and histone acetylation (CHiP-seq) data 392 individuals with all four data types were used for this analysis 2 19 1080 0 3 392 152 20 0 1 40 61 47 17 11 expression methylation acetylation genotype R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 3 / 12
- 8. Quantitative trait loci (QTLs) a QTL is a genetic locus correlated with a phenotype -2 -1 0 1 2 3 -2 -1 0 1 2 -1 0 1 expressionacetylationmethylation 0 1 2 genotype R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 4 / 12
- 9. Quantitative trait loci (QTLs) a QTL is a genetic locus correlated with a phenotype we are interested in QTLs for gene expression (eQTLs), histone acetylation (aceQTLs), and methylation (meQTLs) -2 -1 0 1 2 3 -2 -1 0 1 2 -1 0 1 expressionacetylationmethylation 0 1 2 genotype R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 4 / 12
- 10. Quantitative trait loci (QTLs) a QTL is a genetic locus correlated with a phenotype we are interested in QTLs for gene expression (eQTLs), histone acetylation (aceQTLs), and methylation (meQTLs) QTLs provide a tool to study interaction between other molecular phenotypes -2 -1 0 1 2 3 -2 -1 0 1 2 -1 0 1 expressionacetylationmethylation 0 1 2 genotype R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 4 / 12
- 11. Identifying QTLs R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 5 / 12
- 12. Identifying QTLs ↓ SNPs in 200 kb window Spearman’s ρ R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 5 / 12
- 13. Identifying QTLs ↓ SNPs in 200 kb window Spearman’s ρ ↓ Holm-Bonferroni correction best SNP per feature R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 5 / 12
- 14. Identifying QTLs ↓ SNPs in 200 kb window Spearman’s ρ ↓ Holm-Bonferroni correction best SNP per feature ↓ FDR correction R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 5 / 12
- 15. Removing Principal Components technical, environmental, and biological covariates can swamp out QTL effects 4000 4500 5000 5500 6000 3000 3500 4000 75000 80000 85000 90000 95000 genespeaksCpGs 0 5 10 15 20 PCs removed R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 6 / 12
- 16. Removing Principal Components technical, environmental, and biological covariates can swamp out QTL effects correct by removing principal components 4000 4500 5000 5500 6000 3000 3500 4000 75000 80000 85000 90000 95000 genespeaksCpGs 0 5 10 15 20 PCs removed R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 6 / 12
- 17. Removing Principal Components technical, environmental, and biological covariates can swamp out QTL effects correct by removing principal components number of peaks with a QTL plateaus at 10 PCs, while genes and CpGs continue to increase 4000 4500 5000 5500 6000 3000 3500 4000 75000 80000 85000 90000 95000 genespeaksCpGs 0 5 10 15 20 PCs removed R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 6 / 12
- 18. Removing Principal Components technical, environmental, and biological covariates can swamp out QTL effects correct by removing principal components number of peaks with a QTL plateaus at 10 PCs, while genes and CpGs continue to increase for this analysis, removed 10 PCs from all data 4000 4500 5000 5500 6000 3000 3500 4000 75000 80000 85000 90000 95000 genespeaksCpGs 0 5 10 15 20 PCs removed R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 6 / 12
- 19. Identifying multi-QTLs By intersecting QTL sets, found 240 gene, CpG, and peak triples which shared the same QTL 2984 1799 50981 127 240 1604 2129 eQTL meQTL aceQTL 2984 1799 50981 127 240 1604 2129 eQTL meQTL aceQTL R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 7 / 12
- 20. Identifying multi-QTLs By intersecting QTL sets, found 240 gene, CpG, and peak triples which shared the same QTL 2984 1799 50981 127 240 1604 2129 eQTL meQTL aceQTL 2984 1799 50981 127 240 1604 2129 eQTL meQTL aceQTL Also assessed QTL overlap using π0 approach 100 % 46 % 14 % 31 % 100 % 11 % 83 % 84 % 100 % eQTLs aceQTLs meQTLs eQTLs aceQTLs meQTLs R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 7 / 12
- 21. Bayesian networks Bayesian networks are directed graphical models, where the directed edges represent causal relationships temperature precipitation R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 8 / 12
- 22. Bayesian networks Bayesian networks are directed graphical models, where the directed edges represent causal relationships We use conditional Gaussian networks temperature precipitation Pr(temp) ∼ N(0, 1) Pr(precip | temp) ∼ N(0, 1) R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 8 / 12
- 23. Bayesian networks Bayesian networks are directed graphical models, where the directed edges represent causal relationships We use conditional Gaussian networks Score = likelihood of data given network temperature precipitation Pr(temp) ∼ N(0, 1) Pr(precip | temp) ∼ N(0, 1) 0.7 0.5 R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 8 / 12
- 24. Bayesian networks Bayesian networks are directed graphical models, where the directed edges represent causal relationships We use conditional Gaussian networks Score = likelihood of data given network temperature precipitation Pr(temp) ∼ N(0, 1) Pr(precip | temp) ∼ N(0, 1) 0.7 0.5 Pr(N(0, 1) = 0.7) Pr(N(0.7, 1) = 0.5)× R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 8 / 12
- 25. Networks for QTLs deal and CGBayesNets packages to construct one Bayesian network for each multi-QTL by exhaustive search genotypeexpression acetylation methylation R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 9 / 12
- 26. Networks for QTLs deal and CGBayesNets packages to construct one Bayesian network for each multi-QTL by exhaustive search With deal, edges into genotype were blacklisted genotypeexpression acetylation methylation R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 9 / 12
- 27. Networks for QTLs deal and CGBayesNets packages to construct one Bayesian network for each multi-QTL by exhaustive search With deal, edges into genotype were blacklisted Most common network structure was independence genotypeexpression acetylation methylation R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 9 / 12
- 28. Networks for QTLs deal and CGBayesNets packages to construct one Bayesian network for each multi-QTL by exhaustive search With deal, edges into genotype were blacklisted Most common network structure was independence Accounted for 42% of deal networks, 29% of CGBayesNets networks genotypeexpression acetylation methylation R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 9 / 12
- 29. Future Work Expand the number of multi-QTLs More that just the best SNP per feature Identify overlapping QTLs intelligently More rigourous criterion for number of PCs to remove Try other packages for network learning (HyPhy) Are QTLs enriched in SNPs identified in GWAS studies? Correlations with phenotype (cognitive decline etc.) R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 10 / 12
- 30. Thank you! Harvard / Broad Philip L. D. Jager Lori Chibnik Jishu Xu Charles White Cristin McCabe Towfique Raj Rush David A Bennett Chris Gaiteri Lei Yu Bioinformatics Training Program All the students Sharon Ruschkowski R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 11 / 12
- 31. Software QTL analysis Matrix eQTL qvalue Bayesian networks deal CGBayesNets Slides beamer TikZ tikzDevice Plots pheatmap ggplot2 VennDiagram Colour Scheme solarized R. McCloskey & S. Mostafavi () Omics data integration March 27, 2015 12 / 12