Bayesian Networks as framework for data integration
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1 Bayesian Networks as framework for data integration Jun Zhu, Ph. D. Department of Genomics and Genetic Sciences Icahn Institute of Genomics and Multiscale Biology Icahn Medical School at Mount Sinai New York,
2 Association vs Causality What are Bayesian networks? From Stephen Friend
3 A simple biological question: are there causal/reactive relationships?
4 A Bayesian network approach: Best model
5 A Bayesian network approach: Best models Markov Equivalent models A A A B C B C B C B A C
6 A Bayesian network a causal structure Markov Equivalent models A A B A C B C A B C B C
7 Bayesian network: how to break Markov equivalent? Animal model: mouse F2 intercrosses F0 F1 Diabetes resistant X Diabetes susceptible F2
8 General data flow genetic crosses White adipose Muscle Liver Brain Genotyping clinical traits Molecular profiling Constructing genetics map Scanning QTLs Network reconstruction
9 AACGGTT AACAGTT Causal inference: genetics Perturbations with a causal anchor --Natural variation in a segregating population provides the same type of causal anchor DNA Supporting Gene X Central Dogma of Biology Variation in DNA leads to variation in mrna High expression, alt splicing, codon change, etc. Low expression, no alt. splicing, no codon change, etc. Variation in mrna leads to variation in protein, which in turn can lead to disease Schadt et al. Nature Genetics (2005)
10 A Bayesian network approach: Best models Markov Equivalent models
11 Structure priors based on causality Estimate confidence of causality The pair is independent Bootstrap samples for 200 times Factions of causal, reactive, independent calls The pair is causa/reactive Zhu et al., PLoS CompBio, 2007
12 Bayesian network: integrating genetic data Give a sense of causality to Bayesian network how much improvement is achieved by integrating genetic data?
13 Bayesian Network: a simulation study Zhu et al., PLoS CompBio, 2007
14 Bayesian network: Genetics information is critical when sample size is small Largest improvement in recall occurs with smaller sample sizes Zhu et al., PLoS CompBio, 2007
15 Bayesian network: integrating genetic data Genetic loci L1 L2 Lj Ln-1 Ln Gene G1 G2 Gj Gn-1 Gn Cis-regulation trans-regulation Transcriptional regulation
16 precision 300 samples 900 samples 300 samples 900 samples Weak signals recall Strong signals
17 Bayesian network: why samples matter?
18 Bayesian network: integrating genetics Experimental Hsd11b1 signature : mice treated with Hsd1 inhibitor Prediction Hsd1 signatures based on BxD data Correlation to Hsd1 10% of predicted signature overlap with experimental one BN without genetics 20% of predicted signature overlap with experimental one BN with genetics 52% of predicted signature overlap with experimental one Zhu J et al, Cytogenet Genome Res. (2004)
19 A framework for data integration High throughput data knowledge Medline Biocarta/Biopathway Biologists Microarray data Proteomic data Metabolomic data probabilistic graphic models Database Genomics Genetics Hypothesis, test GUI
20 Bayesian network: PPI Zhu J et al, Nature Genetics, 2008
21 Bayesian network: PPI 4-clique 4-clique 3-clique 3-clique Clique community (partial clique) Zhu J et al, Nature Genetics, 2008
22 Bayesian network: PPI Zhu J et al, Nature Genetics, 2008
23 Bayesian network: Transcription Factors Is the TF is functional? Are genes B, C, D, and E are correlated? TF B C D E
24 Bayesian network: Transcription Factors Introducing scale-free priors for TF or protein complex p( T g) w( T) w( T) log( g i R r( T, g i ) r cutoff ) Zhu J et al, Nature Genetics, 2008
25 Yeast segregants Public databases Synthetic complete medium Logorithm growth Proteinprotein interations Gene expression genotypes Transcription factor binding sites Protein Metabolite interations Bayesian network Zhu J et al, Nature Genetics, 2008
26 Integration improves network qualities BN KO data GO terms TF data w/o any priors w/ genetics priors w/ genetics, TF and PPI priors Zhu J et al, Nature Genetics, 2008
27 Prospective validation is the gold standard ILV6 gives rise to large expression signature GCN4 ILV6 KO sig enriched (p~10e-52) GCN4 upregulated in ILV6 KO large signature ILV6 LEU2 KO gives rise to small expression signature LEU2 KO sig enriched (p~10e-18) GCN4 downregulated in LEU2 KO small signature GCN4 LEU2 Zhu J et al, Nature Genetics, 2008
28 How does LEU2 affect LEU3 activity? LEU3 binding sites mrna expression LEU2 LEU3 LEU2 Surrogate marker for Leu3p activity
29 A framework for building causal networks High throughput data knowledge Medline Biocarta/Biopathway Biologists Microarray data Proteomic data Metabolomic data probabilistic graphic models Database Genomics Genetics Hypothesis, test GUI
30 Yeast segregants Yeast segregants Public databases Synthetic complete medium Logorithm growth Proteinprotein interations Gene expression metabolites genotypes Transcription factor binding sites Protein Metabolite interations Bayesian network Zhu et al, PLoS Biology, 2012
31 Metabolite abundance is under genetic control Zhu et al, PLoS Biology, 2012
32 KEGG biochemical pathways d m, e p ( m e) e Zhu et al, PLoS Biology, 2012
33 LEU2 mrna is causal to 2-isopropylmalate KEGG pathway Zhu et al, PLoS Biology, 2012
34 LEU3 binding site LEU2 With metabolomic data
35 LEU3 regulation The activity of Leu3p is positively regulated by alphaisopropylmalate (IPM), the product of the first step in leucine biosynthesis Sze JY, et al. (1992) In vitro transcriptional activation by a metabolic intermediate: activation by Leu3 depends on alphaisopropylmalate. Science 258(5085): The degree of activation by Leu3p is Leu3p concentration dependent, and it has been shown that LEU3 gene expression is regulated by general amino acid control, which is mediated by the GCN4 transcription factor Zhou K, et al. (1987) Structure of yeast regulatory gene LEU3 and evidence that LEU3 itself is under general amino acid control. Nucleic Acids Res 15(13):
36 2-isopropylmalate: mechanism of causal regulator LEU2 LEU2 genotype LEU2 activity 2-isopropylmalate Transcriptional response for genes with LEU3 binding sites LEU3 activity
37 Consistent with KEGG pathway Zhu et al, PLoS Biology, 2012
38 What else can you learn from integrating metabolomic data? Metabolite QTLs Causal candidates Metabolite Signature size KO Protein degradation
39 Zhu et al, PLoS Biology, 2012
40 Is the transcriptional effect real? Zhu et al, PLoS Biology, 2012
41 PHM7-ko affects many metabolites Zhu et al, PLoS Biology, 2012
42 Integration of CNV blocks into Bayesian networks Network-based model selection Random gene Tran et al. BMC Sys. Biol. 2011
43 Aknowledgements Sage Bionetworks Stephen Friend et al. Mount Sinai Eric Schadt Bin Zhang Zhidong Tu Decode Valur Emilsson UCLA Jake Lusis Xia Yang, et al U Washington Roger Baumgarner Berkerley Rachel Brem Princeton Lenoid Kruglyak Harvard Jun Liu U Wisconsin Alan Attie Mark Keller, et al Merck Qiuwei Xu Ethan Xu Theretha Zhang Fred Hutchingson Paddison lab MD Anderson Hanash lab Mount Sinai Powell lab Oh Lab Casaccia
44 Aknowledgements Icahn Institute of Genomics and Multiscale Biology, Icahn Medical School at Mount Sinai Canary Foundation Prostate Cancer Foundation NIH NCI
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