Using Network Flow to Bridge the Gap Using Network Flow to Bridge - - PowerPoint PPT Presentation

Using Network Flow to Bridge the Gap Using Network Flow to Bridge the Gap between Genotype and Phenotype Teresa Przytycka NIH / NLM / NCBI NIH / NLM / NCBI

Journal “Wisla” (1902) Picture from a local fare in Lublin Poland from a local fare in Lublin, Poland

Phenotypes Genotypes

Journal “Wisla” (1902) Picture from a local fare in Lublin Poland from a local fare in Lublin, Poland

G 1

Association studies

Genome 1 Genome 2 Genome 3 Genome n

Genotype: effects of genotypic effects of genotypic variation:

• change in amino acid
• change in gene structure
• copy number variations ….

5

Genotype: Phenotype (e.g. disease) effects of genotypic effects of genotypic variation:

• change in amino acid
• change in gene structure
• copy number variations ….

6

G l Goals :

• A method for system level analysis of propagation of

y y p p g such perturbation in the network

• Prediction of “causal” mutations
• Prediction of master regulators (network hubs)

involved in disease

• Prediction of pathways dys-regulated in disease

Propagation of the effects of Copy number aberrations in Glioma

Cancer Cases G i d t CNV

Gene 1 Gene 2 Gene 3

Gene expression data . . mosomes . . . chrom I t t d

Gene n

Integrated Protein-protein, protein-DNA phosphorylation network

Copy number aberrations py

• r/and mutations

Gene expression

Copy number aberrations py

• r/and mutations

Gene expression

Signature genes

Copy number aberrations py

• r/and mutations

Signature genes

Copy number aberrations py

• r/and mutations

Signature genes

Method outline Method outline

• 1. Selecting marker genes to be used as “phenotype”
• 2. Genotype-phenotype association
• 3. Uncovering information flow between genotype and

phenotype

• 4. Inferring dys- regulated, genes, pathways, and causal

mutations

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Selecting “phenotype” genes

C C

Gene 1 Gene 2

Cancer Cases Gene expression data

Gene 2 Gene 3

. . . . . target genes

Gene n

Selecting “phenotype” genes

Selecting “phenotype” genes

Smallest set of genes so that each case is “covered” at least specified number of times

Associations between copy number variations and gene expression of selected target genes and gene expression of selected target genes

Cancer Cases Gene expression data Cancer Cases CNV data

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Significant correlation between CNV and expression

Cancer Cases

expression

Gene 1 Gene 2 Gene 3

Gene expression da . . . . .

Gene n

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Significant correlation between CNV and expression

Cancer Cases

expression

Gene expression da target gene locus

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Significant correlation between CNV and expression

Cancer Cases

expression

Gene expression da target gene candidate causal genes candidate causal genes

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Uncovering pathways of information flow between CNV and target gene CNV and target gene

Cancer Cases Gene expression da

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Using expression to guide path discovery

Cancer Cases Gene expression da

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Translating probabilities it resistances

Cancer Cases Gene expression da

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Resistance - set to favor most likely path -based on gene expression values

(reversely proportional to the average correlation of the expression of the adjacent genes with expression of the target gene)

Finding subnetworks with significant current flow

Cancer Cases Gene expression da

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Resistance - set to favor most likely path -based on gene expression values

(reversely proportional to the average correlation of the expression of the adjacent genes with expression of the target gene)

G l Goals :

• A method for system level analysis of propagation of

y y p p g such perturbation in the network

• Prediction of “causal” mutations
• Identification master regulators (network hubs)

involved in disease

• Identification pathways dys-regulated in disease

Putative causal variation

(with lots of additional caveats) Cancer Cases (with lots of additional caveats) Gene expression da

26

Resistance - set to favor most likely path -based on gene expression values

(reversely proportional to the average correlation of the expression of the adjacent genes with expression of the target gene)

Causal copy number aberrations Causal copy number aberrations

27 27

G l Goals :

• A method for system level analysis of propagation of

y y p p g such perturbation in the network

• Prediction of “causal” mutations
• Prediction “master regulators” (network hubs) involved

in disease

• Prediction pathways dys-regulated in disease

Solve current flow for all pairs and find nodes belonging to many paths g g y p

Cancer Cases Gene expression data Cancer Cases CNV data

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Hubs Hubs

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G l Goals :

• A method for system level analysis of propagation of

y y p p g such perturbation in the network

• Prediction of “causal” mutations
• Prediction of “master regulators” (network hubs)

involved in disease

• Prediction of pathways dys-regulated in disease

Are there common functional pathways?

Cancer Cases Gene expression dat Cancer Cases CNV data

32

Common GO pathways

33

G l Goals :

• A method for system level analysis of propagation of

y y p p g such perturbation in the network

• Prediction of “causal” mutations
• Prediction of “master regulators” (network hubs)

involved in disease

• Prediction of pathways dys-regulated in disease

Design details under the hood g

• Current flow reduces to solving a set of linear equations (Kirchhoff's

laws) Caveat: We had to solving a linear system with 20,000 variables thousands of times for permutation test required new methodology

• Many biological interactions are directional. This can be taken care by

solving linear program with corresponding constraints - Caveat: the network is to big for solving thousands of linear programs network is to big for solving thousands of linear programs

• Null model and p-value estimations

Kim, Wuchty, Przytycka – PloS Comp Bio 2011

Kim, Przytycki, Wuchty, Przytycka – Phys. Bio. 2011

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Acknowledgments

Group members: Yoo-Ah Kim DongYeon Cho Yang Huang Jan Hoinka Xiangjun Du g g Damian Wojtowicz Raheleh Salari

Stefan Wuchty (NCBI)

Collaborators:

Journal “Wisla” (1902) Picture from a local fare in Lublin, Poland

Jozef Przytycki (GWU) Stefan Wuchty (NCBI)

my great-great uncle (the “Giant”)

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Acknowledgments

Group members: Collaborators: Yoo-Ah Kim DongYeon Cho

B i Oli (NIDDK) Stefan Wuchty (NCBI)

Yang Huang

Brian Oliver (NIDDK) John Malone Nicolas Mattiuzzo J ti A d (I di U i it )

Jan Hoinka Xiangjun Du g g

Justin Andrews (Indiana University) Jozef Przytycki (GWU)

Damian Wojtowicz Raheleh Salari

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Impact of gene copy number on gene expression in Drosophila melanogaster expression in Drosophila melanogaster

ge (log2)

• ld chang

ression fo

• 1

E i ( ild t ) Exp

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Expression (wild type) collaboration with Brian Oliver group (NIDDK)

CNV-related perturbations propagate t h i t ti t k trough interaction network

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Co-complex network from Artavanis-Tsakonas group (unpublished)

Impact on copy number on gene i i li expression in glioma

CNV Chromosomes Correlation between CNV and expression

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Genotype: effects of genotypic effects of genotypic variation:

• change in amino acid
• change in gene structure
• copy number variations ….

43

Phenotype Genotype: effects of genotypic effects of genotypic variation:

• change in amino acid
• change in gene structure
• copy number variations ….

44

Phenotype Genotype: effects of genotypic effects of genotypic variation:

• change in amino acid

Molecular phenotypes

• change in gene structure
• copy number variations ….

phenotypes

• gene expression
• Metabolite level

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Copy number variations (CNV) (gene dosage)

• implicated in large number of human diseases (cancer, Crohn's disease,

autism)

(gene dosage)

• 28,025 structural variants identified in 1000 genome study (2,000 changes

affecting full genes or exons)

• Frequent type of somatic mutations in cancer

Phenotype Genotype: Molecular phenotypes phenotypes

• gene expression
• Metabolite level

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