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Differential Weighted Gene Coexpression Network Analysis
Applied to Mouse Weight
Differential Weighted Gene Coexpression Network Analysis
Applied to Mouse Weight
Tova FullerSteve Horvath
Department of Human GeneticsUniversity of California, Los Angeles
ICSB, 10/5/07
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Outline
• Introduction: – Single versus differential
network analysis
• Differential Network construction
• Results• Functional Analysis• Conclusion
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Goals of Single Network Analysis
• Identifying genetic pathways (modules)
• Finding key drivers (hub genes)• Modeling the relationships between:
– Transcriptome– Clinical traits / Phenotypes– Genetic marker data
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Validation set 1 Validation set 2
Single Network WGCNA
1 gene co-expression networkMultiple data sets may be used for
validation
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Goals of Differential Network Analysis
• Uncover differences in modules and connectivity in different data sets– Ex: Human versus chimpanzee brains
(Oldham et al. 2006)
• Differing toplogy in multiple networks reveals genes/pathways that are wired differently in different sample populations
Oldham MC, Horvath S, Geschwind DH (2006) Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc Natl Acad Sci U S A 103, 17973-17978.
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NETWORK 1
Differential Network WGCNA
2+ gene co-expression networksIdentify genes and pathways that are:
1. Differentially expressed2. Differentially wired
NETWORK 2
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• Single network analysis female BxH mice revealed a weight-related module (Ghazalpour et al. 2006)
• Samples: Constructed networks from mice from extrema of weight spectrum:– Network 1: 30 leanest mice– Network 2: 30 heaviest mice
• Transcripts: Used 3421 most connected and varying transcripts
BxH Mouse Data
Ghazalpour A, Doss S, Zhang B, Wang S, Plaisier C, Castellanos R, Brozell A, Schadt EE, Drake TA, Lusis AJ, Horvath S (2006) Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS genetics 2, e130
NETWORK 1 NETWORK 2
135 FEMALES
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Methods
Compute Comparison MetricsCompute Comparison Metrics• Difference in expression: t-test statisticDifference in expression: t-test statistic• Compare difference in connectivity: Compare difference in connectivity: DiffKDiffK
Compute Comparison MetricsCompute Comparison Metrics• Difference in expression: t-test statisticDifference in expression: t-test statistic• Compare difference in connectivity: Compare difference in connectivity: DiffKDiffK
Identify significantly different genes/pathwaysIdentify significantly different genes/pathwaysPermutation testPermutation test
Identify significantly different genes/pathwaysIdentify significantly different genes/pathwaysPermutation testPermutation test
Functional analysis of significant genes/pathwaysFunctional analysis of significant genes/pathwaysDAVID databaseDAVID database
Primary literaturePrimary literature
Functional analysis of significant genes/pathwaysFunctional analysis of significant genes/pathwaysDAVID databaseDAVID database
Primary literaturePrimary literature
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Computing Comparison Metrics
DIFFERENTIAL EXPRESSION
t-test statistic computed for each gene, t(i)
DIFFERENTIAL CONNECTIVITY
K1(i) = k1(i) K2(i) = k2(i) max(k1)
max(k2)
DiffK(i): difference in normalized connectivities for each gene:
DiffK(i) = K1(i) – K2(i)
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Sector Plot
We visualize the comparison metrics via a sector plot:
• x-axis: DiffK
• y-axis: t statistics
We establish sector boundaries to identify regions of differentially expressed and/or connected regions
• |t| = 1.96 corresponding to p = 0.05
• |DiffK| = 0.4
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no.perms: number of permutations
For each sector j, we compare the number of genes in unpermuted and permuted sectors (nobs and nperm)
Permutation test:Identifying significant sectors
€
p j =# times (nobs
j ≤ npermj ) +1
no.perms+1
NETWORK 1 NETWORK 2
PERMUTE
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Sector Plot Results
0.010.001
0.001 0.001X
X X
X
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Functional AnalysisSECTOR 3
High t statistic High DiffK
Yellow module in leanGrey in obese
(63 genes)
Genes in these sectors have higher connectivity in lean than obese mice: ~ pathways potentially
disregulated in obesity ~
SECTOR 5Low t statistic
High Diff K(28 genes)
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Sector 3:Functional Analysis Results
DAVID Database• “Extracellular”:
– extracellular region (38% of genes p = 1.8 x 10-4)– extracellular space (34% of genes p = 5.7 x 10-4)
• signaling (36% of genes p = 5.4 x 10-4)• cell adhesion (16% of genes p = 7.7 x 10-4)• glycoproteins (34% of genes p = 1.6 x 10-3) • 12 terms for epidermal growth factor or its related proteins
– EGF-like 1 (8.2% of genes p = 8.7 x 10-4), – EGF-like 3 (6.6% of genes p = 1.6 x 10-3), – EGF-like 2 (6.6% of genes p = 6.0 x 10-3), – EGF (8.2% of genes p = 0.013)– EGF_CA (6.6% of genes p = 0.015)
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Sector 3:Functional Analysis Results
Primary Literature
• Results supported by a study on EGF levels in mice (Kurachi et al. 1993)– EGF found to be increased in obese mice– Obesity was reversed in these mice by:
• Administration of anti-EGF • Sialoadenectomy
Kurachi H, Adachi H, Ohtsuka S, Morishige K, Amemiya K, Keno Y, Shimomura I, Tokunaga K, Miyake A, Matsuzawa Y, et al. (1993) Involvement of epidermal growth factor in inducing obesity in ovariectomized mice. The American journal of physiology 265, E323-331
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Sector 5: Functional Analysis Results
DAVID Database
• Enzyme inhibitor activity (p = 2.9 x 10-3)*• Protease inhibitor activity (p = 6.0 x 10-3)• Endopeptidase inhibitor activity (p = 6.0 x 10-3)• Dephosphorylation (p = 0.012)• Protein amino acid dephosphorylation (p =
0.012)• Serine-type endopeptidase inhibitor activity (p
= 0.042)
* p values shown are corrected using Bonferroni correction
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Itih1 and Itih3• Enriched for all categories shown previously• Located near a QTL for hyperinsulinemia (Almind and
Kahn 2004)• Itih3 identified as a gene candidate for obesity-related
traits based on differential expression in murine hypothalamus (Bischof and Wevrick 2005)
Serpina3n and Serpina10• Enriched for enzyme inhibitor, protease inhibitor, and
endopeptidase inhibitor• Serpina10, or Protein Z-dependent protease inhibitor (ZPI) has
been found to be associated with venous thrombosis (Van de Water et al. 2004)
Sector 5: Functional Analysis Results
Primary Literature
Almind K, Kahn CR (2004) Genetic determinants of energy expenditure and insulin resistance in diet-induced obesity in mice. Diabetes 53, 3274-3285 Bischof JM, Wevrick R (2005) Genome-wide analysis of gene transcription in the hypothalamus. Physiological genomics 22, 191-196 Van de Water N, Tan T, Ashton F, O'Grady A, Day T, Browett P, Ockelford P, Harper P (2004) Mutations within the protein Z-dependent protease inhibitor gene are associated with venous thromboembolic disease: a new form of thrombophilia. Bjh 127, 190-194
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Conclusions
• Differential Network Analysis reveals pathways that are both differentially regulated and connected in mouse obesity– Genes that are differentially connected may/may not be
differentially expressed
• Primary literature supports biological plausibility of these pathways in weight related disorders– Sector 3 & EGF pathways: potential EGF causality in
obesity– Sector 5 & serine protease pathways: potential link
between obesity and venous thrombosis
• These results help identify targets for validation with biological experiments
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AcknowledgementsAcknowledgementsGuidance
HORVATH LABSteve HorvathJason AtenJun DongPeter LangfelderAi LiWen LinAnja PressonLin WangWei Zhao
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An R tutorial may be found at:http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/DifferentialNetworkAnalysis
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Collaboration
LUSIS LABJake LusisAnatole GhazalpourThomas Drake
Funding
Genomic Analysis Training Grant
UCLA Medical Scientist Training Program (MD/PhD)