1. Is Forkhead Box N1 (FOXN1) significant in both men and women diagnosed with Chronic Fatigue Syndrome? Charlyn Suarez

2. Mentors Jeanette Papp Anja Presson

3. 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

4. Outline Define Chronic Fatigue Syndrome Dataset/Analyses Weighted Gene Co-Expression Network Method Previous Analysis My Analysis Conclusion Additional Analyses Future Goal

5. 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/cfshttp://www.cdc.gov/cfs

6. 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

7. 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

8. 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)

9. 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

10. Overview of Weighted Gene Co- Expression Network Developed by Steve Horvath Biostatistics & Human Genetics Department University of California, Los Angeles

11. Biology and Networks components of a living cell are dynamically interconnected encoded into a complex intracellular web of molecular interaction can be represented as a network connectivity within networks can be an important variable for identifying important nodes (genes)

12. Gene Co-Expression Network each gene corresponds to a node two genes are connected by an edge if their expression values are correlated

13. Networks can be represented by an adjacency matrix, A = [a ij ] a ij =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

14. Connection Strength Gene X Gene Y Sample 112 Sample 225 Sample 336 GeneXY X1.9608 Y 1 |cor(x,y)| |cor(x,y)| 14 GeneXY X1.5713 Y 1

15. Identifying Modules Gene co-expression modules in the network were identified using average linkage hierarchical clustering

16. 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

17. Gene Significance modules correlation between gene expression and cluster trait (severity of CFS) mean gene significance

18. 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 module FOXN1 is fairly connected in green module Other selection criteria in original gene selection Trait correlation SNP correlation (Presson et al, CAMDA 2006)

19. Modules for Males (dendrogram)

20. Gene Significance for Males modules mean gene significance

21. Modules for Females dendrogram modules (dendrogram)

22. Gene Significance for Females modules mean gene significance

23. Conclusions FOXN1 is associated with CFS severity in men and women FOXN1 is differentially expressed in men and women (higher expression in women)

24. 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

25. 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)

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

27. Acknowledgement UCLA Genotyping & Sequencing Core Anja Presson Jeanette Papp Steve Horvath SoCalBSI Jamil Momand Wendie Johnston Sandra Sharp Nancy Warter- Perez NIH/NSF

28. 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 (a ij = |cor(x i, x j )| β )

29. 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

30. 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

31. Hierarchical Clustering