Is Forkhead Box N1 (FOXN1) significant in both men and women

diagnosed with Chronic Fatigue Syndrome?

Charlyn Suarez

Mentors

Jeanette Papp

Anja Presson

Why?

Males and females are genetically different

Studies have shown genetic factors associated with a disease and/or a drug response appear to be different between male and female patients

Outline

Define Chronic Fatigue Syndrome

Dataset/Analyses

Weighted Gene Co-Expression Network Method

Previous Analysis

My Analysis

Conclusion

Additional Analyses

Future Goal

Chronic Fatigue Syndrome(CFS)

Complex disease characterized by profound fatigue Not improved by bed rest Worsened by physical and mental activities

Affects women at four times the rate of men

Cause remains unknown Genetics and environment The roles of the immune, endocrine and nervous

systems

Source: http://www.cdc.gov/cfs

Dataset

Comes from the CDC Chronic Fatigue Syndrome Research Group Contains microarray, SNP, and clinical data

Consists of 98 women and 29 men

Is restricted to genes that showed some sign of differential expression between CFS patients and controls

Previous Analysis

Has shown that FOXN1 is a candidate gene for CFS using the Weighted Gene Co-Expression Network Method (Presson et al., CAMDA 2006)

FOXN1 is differentially expressed in CFS patients and controls

R

Biological Significance of FOXN1

Mutations in mice & humans cause: Nudity Depleted immune system due to dysfunctional T-cells

Highly expressed in thymus epithelia cells: Convert lymphocytes to T-cells Release functional T-cells to fight infection

(Nehls et al. 1994; Pignata et al., 1996; Adriani et al. 2004)

CFS patients have an overactive immune system & high T-cell production (Maher et al. 2005)

Analysis

determine if FOXN1 is significant if the dataset is restricted to men or women

form a Weighted Gene Co-Expression Network for men and women separately

R

Overview of Weighted Gene Co-Expression Network

Developed by Steve HorvathBiostatistics & Human Genetics Department

University of California, Los Angeles

Biology and Networks

components of a living cell are dynamically interconnectedencoded into a complex intracellular web of molecular interactioncan be represented as a networkconnectivity within networks can be an important variable for identifying important nodes (genes)

Gene Co-Expression Network

each gene corresponds to a node

two genes are connected by an edge if their expression values are correlated

Networks can be represented by an adjacency matrix, A = [aij]

aij=connection strength between a pair of genes (0 ≤ aij ≤ 1 for all 1 ≤ i,j ≤ n)

V1 V2 V3 V4 V5

V1 1 .2 0 .1 .5

V2 0 .2 0 .3 0

V3 0 0 .8 0 .1

V4 0 0 0 1 0

V5 0 .9 0 0 1

Connection Strength

Gene

X

Gene

Y

Sample 1 1 2

Sample 2 2 5

Sample 3 3 6

Gene X Y

X 1 .9608

Y .9608 1

|cor(x,y)|

|cor(x,y)| 14

Gene X Y

X 1 .5713

Y .5713 1

Identifying Modules

Gene co-expression modules in the network were identified using average linkage hierarchical clustering

Modules for Original Analysis

Branches-clusters of similarly expressed genes

Modules-branches of the dendrogram

Trimmed to define 4 modules

grey color-genes that did not belong to any module

dendrogram

Gene Significance

0.00

0.05

0.10

0.15

0.20

CLUSTER0 , p-value= 2.5e-63

modules

correlation between gene expression and cluster trait (severity of CFS)

mea

n ge

ne s

igni

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Connectivity/Gene Selection Criteria

Intramodular connectivity for each gene is the sum of the connection strengths between that gene and all other genes in its moduleFOXN1 is fairly connected in green moduleOther selection criteria in original gene selection Trait correlation SNP correlation(Presson et al, CAMDA 2006)

Modules for Males(dendrogram)

Gene Significance for Males

blue brown grey turquoise

0.00

0.05

0.10

0.15

0.20

CLUSTER0 - Males , p-value= 3.4e-33

modules

mea

n ge

ne

sign

ific

ance

Modules for Females

dendrogram

modules

(dendrogram)

Gene Significance for Females0.00

0.05

0.10

0.15

0.20

CLUSTER0 - Females , p-value= 2.6e-74

modules

mea

n ge

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igni

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Conclusions

FOXN1 is associated with CFS severity in men and women

FOXN1 is differentially expressed in men and women (higher expression in women)

Additional Analysis

Compare the gene significance between men and women in the original green module

Determine if the module structure from the combined analysis is preserved in the male and female analyses

Future Goal

To include my analysis in a paper that will be published in the proceedings for the Critical Assessment of Microarray Data Analysis (CAMDA)

References

http://www.cdc.gov/cfs

Anja Presson, Eric Sobel, Jeanette Papp, Aldons J. Lusis, Steve Horvath. Integration of Genetic and Genomic Approaches for the Analysis of Chronic Fatigue Syndrome Implicates Forkhead Box N1. http://www.camda.duke.edu/camda06/papers/

Pinsonneault J, Sadée W. Pharmacogenomics of Multigenic Diseases: Sex-Specific Differences in Disease and Treatment Outcome. AAPS PharmSci. 2003; 5 (4): article 29. DOI: 10.1208/ps050429

http://www.blackwellpublishing.com/press/pressitem.asp?ref=832 (Higher Mortality Rate For Females Undergoing Heart Surgery)

Acknowledgement

UCLA Genotyping &Sequencing Core Anja Presson Jeanette Papp Steve Horvath

SoCalBSI Jamil Momand Wendie Johnston Sandra Sharp Nancy Warter- Perez

NIH/NSF

Connection Strength

absolute value of the Pearson correlation coefficient was calculated for all pair-wise comparisons of gene-expression values across all microarray samples

correlation matrix was then transformed into a matrix of connection strengths using a power function (aij = |cor(xi, xj)|

β)

Dendrogram

Hierarchical clustering may be represented by a two dimensional diagram known as dendrogram

A dendrogram is a tree diagram frequently used to illustrate the arrangement of the clusters produced by a clustering algorithm (see cluster analysis)

Dendrograms are often used in computational biology to illustrate the clustering of genes

Hierarchical Clustering

Central to all of the goals of cluster analysis is the notion of degree of similarity (or dissimilarity) between the individual objects being clustered

agglomerative methods-proceed by series of fusions of the n objects into groups

divisive methods-separate n objects successively into finer groupings

Hierarchical Clustering