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Mapping Transcription Mechanisms from Multimodal Genomic Data

Hsun-Hsien Chang, Michael McGeachie, and Marco F. Ramoni

Children’s Hospital Informatics ProgramHarvard-MIT Division of Health Sciences and Technology

Harvard Medical School March 10, 2010

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Harvard Medical School

Information Flow in Multimodal Genomic Data

• Genetic Variants– 100k – 1000k SNPs– 250k copy number

variations (CNVs)– 250k methylation

measurements

• Transcripts– 50k mRNA expression levels– 50k microRNA expression levels– 1.5M exon expression / splicing

Information

Information

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Expression Quantitative Trait Loci (eQTLs)• Connection from variant to expression is an

information channel– A DNA locus is modulating the expression level of

a gene = eQTL• Cis(Trans) eQTLs are the genetic variants

located close to (far away) genes.• Identifying cis-eQTLs is easier

– Focusing on cis-eQTL reduces search space– trans eQTLs?

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• Cancer: based on genetic modification (variants) and cellular malfunction (gene expression)

• Identification of eQTLs helps understand molecular mechanisms in cancer and provides biological insight.

• Clinical study of Acute lymphoblastic leukemia (ALL)– The most common malignancy in children, nearly one third of all

pediatric cancers.– A few cases are associated with inherited genetic syndromes (i.e., Down

syndrome, Bloom syndrome, Fanconi anemia), but the cause remains unknown.

• Data– 29 patients.– Genotyped 100,000 SNPs (Affymetrix Human Mapping 100K).– Profiled 50,000 gene expressions (Affymetrix HG-U133 Plus 2.0).

Clinical Study on Pediatric Leukemia

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Challenges in Finding eQTLs

• Compare the distribution of each Variant to the levels of each expression measurement– Computational

• All pairs of variants vs. expressions is costly• Usually discretize expression levels (Pensa et al., BioKDD, 2004)

– Multiple testing considerations• Understanding

– Too many associations to test via laboratory science• Computational methods of biological discovery• Want to summarize main informational (biological) pathways

• Answer: Use transcriptional information

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Transcriptional Information Channel

X Y

SNPs are modeled as binomial variables.

Expressions are modeled as log-normal variables.

• Mutual Information quantifies information flow:

• Higher MI is achieved by larger σ2 and smaller σk2 , i.e., when expression level Y is more likely modulated by SNP X.

Transcription Channel

• Info Theory:measures Entropy,H(X)

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• Transcript Y is modulated by SNP X:

• Transcript Y is independent of SNP X:

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Transcriptional Information Map

X1 Y1

Y2

X4 Y4

X5 Y5

X6

X7 Y7

Y8

X9 Y9

X8

Y3

Y6

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ALL Transcriptional Information Map of Chr21

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Cluster Genes and SNPs into Networks

X1 Y1

X2 Y2

X3

X4 Y4

X5 Y5

X6

X7 Y7

Y8

X9 Y9

X8

Y3

Y6

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X1 Y1

Y2

X3

X4

Y9

X8

Cluster Genes and SNPs into Networks

• We can further infer the optimal modulation patterns using Bayesian networks.

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• Bayesian networks are directed acyclic graphs: – Nodes correspond to random variables.– Directed arcs encode conditional probabilities of the target nodes on the source nodes.

– p(X) depends on (A,B)– p(Z|X,Y) independent of (A,B)

Bayesian Networks

A

B

X

Y Z

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Infer Bayesian Networks in Individual Clusters

Y1

Y2

Y9• Step 1: Use TIM as the initial network.• Step 2: Bayesian network infers SNP-SNP connections.

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A Bayesian Network Inferred from Chr21 TIM

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Information Theoretic Network Analysis

• Find hubs, motifs, guilds, etc.– Abstract edges– Global patterns -> local patterns– Reveal emergent properties– Information theoretic approach using Data

Compression

• Alterovitz G, and Ramoni MF, “Discovering biological guilds through topological abstraction,” AMIA Annu Symp Proc, pp. 1-5, 2006.

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Identified Fundamental Components

Reference: Alterovitz and Ramoni, AMIA Annu Symp Proc, pp. 1-5, 2006.

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Identification of Cis- and Trans eQTL

• RIPK4, 21q22.3– Related to Downs

Syndrome– RIPK4 has 5

(trans) SNPs in q11.2 (shown as blue in the figure) affecting its expression.

RIPK4

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Identification of Cis and Trans eQTL• CYYR1, 21q21.1

– Recently discovered. – Encodes a cysteine and

tyrosine-rich protein.– Recent study found a

correlation with neuroendocrine tumors.

– TIM shows CYYR1 modulated by SNPs across the q arm of chromosome 21.

– DSCAM related to Down’s syndrome

– DSCAM-CYYR1 interaction leads to ALL?

DSCAM

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Complete TIM Algorithm

Infer Network in Individual

Clusters

Cluster 1

Cluster N

...

...

...

...

...

Compute Transcriptional

Information

...

...

...

...

Genetic Variant Transcript

Group Linked SNPs and Transcripts

Cluster 1

Cluster N

. . .

Network Topology

Analysis and Summary

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Transcriptional Information Maps

• Make large multimodal genetic dataset amenable to transcriptional analysis

• Identifies– Modulation patterns between genetic variants

and transcripts.– CIS and TRANS eQTL.

• Analysis of pediatric ALL helps identify biological hypotheses regarding connection to Down’s syndrome

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Questions?Thanks to

Prof. Marco F. Ramoni, Dr. Hsun-Hsien Chang, Dr. Gil Alterowitz, Children’s

Hospital Informatics Program, Brigham and Women’s Hospital