Integration of Genetic and Genomic Approaches for the Analysis of Chronic Fatigue Syndrome Implicates Forkhead Box N1

Transcription

1 Integration of Genetic and Genomic Approaches for the Analysis of Chronic Fatigue Syndrome Implicates Forkhead Box N1 Angela Presson, Jeanette Papp, Eric Sobel, and Steve Horvath Biostatistics and Human Genetics University of California, Los Angeles CAMDA

2 CAMDA 2006 Challenge DNA Level: ~ 50 Pre-selected SNP s 2. Relate SNP data to Expression data mrna Level: ~ 20K genes/array 1. Relate Expression data to Clinical Trait data Organism Level: ~ 70 Clinical Traits 3. Integrate results to find CFS relevant genes. CAMDA

3 Analysis Overview 1. Construct gene co-expression network from microarray data. (Zhang and Horvath 2005) 2. Identify module of interest using trait data. 3. Determine informative SNP s and relate them to gene co-expression network. 4. Identify genes with statistical and biological significance. 5. Choose subset of CFS and control samples for validating the candidate biomarker. CAMDA

4 Network = Adjacency Matrix A network can be represented by an adjacency matrix, A=[a ij ], that encodes connection strength between a pair of genes. Two genes have high connection strength if they have similar expression patterns. A is a symmetric matrix with entries in [0,1]. Two Network Models: Unweighted: a ij = 1 if two genes are adjacent (connected) and 0 otherwise. Weighted: each a ij gives the connection strength between gene pairs. CAMDA

5 Important Task in Many Genomic Applications: Given a network (pathway) of interacting genes how to find the central players? CAMDA

6 Identifying Key Players of Interest Imagine you wanted to recruit students to your science program. Popularity alone might suggest the head cheerleader or quarterback. Head Cheerleader Star Quarterback CAMDA

7 But, the head of the chess club would probably be a better bet! Chess Club President Cheerleader Quarterback CAMDA

8 Two Network Definitions 1. Number of friends = Connectivity Gene connectivity = row sum of the adjacency matrix, sum of gene i s connection strengths. k i = j a ij 2. Chess Club, Sport Teams = Modules Gene Module = cluster of highly connected (similarly expressed) genes in a network. CAMDA

9 Gene connectivity vs. Module connectivity Whole network connectivity Intra-modular connectivity Whole network connectivity is largely driven by the size of the module containing the gene. Connectivity within a module is biologically & mathematically more meaningful than whole network connectivity. CAMDA

10 Analysis Overview 1. Construct gene co-expression network from microarray data. data. (Zhang and Horvath 2005) 2. Identify module of interest using trait data. 3. Determine informative SNP s and relate them to gene co-expression network. 4. Identify genes with statistical and biological significance. 5. Choose subset of CFS and control samples for validating the candidate biomarker. CAMDA

11 Revisiting the Adjacency Matrix Connection Strength (Adjacency) vs. Correlation Step function (hard thresholding) is indicated by the black, solid line. Adjacency Adjacency a ij = cor(gene i, gene j ) β. Correlation Power adjacency functions (soft thresholding) are indicated by colored, dashed lines. Once we found an appropriate β (according to methodology outlined in Zhang and Horvath 2005) we found that our network results were robust to small changes in β. CAMDA

12 Four Modules Identified Using Hierarchical Clustering Brown Red Turquoise Green Grey colors indicate genes outside of any module. MDS plot indicates clear separation of brown, green, turquoise modules. CAMDA

13 Analysis Overview 1. Construct gene co-expression network from microarray data. (Zhang and Horvath 2005) 2. Identify module of interest using trait data. 3. Determine informative SNP s and relate them to gene co-expression network. 4. Identify genes with statistical and biological significance. 5. Choose subset of CFS and control samples for validating the candidate biomarker. CAMDA

14 A clinical trait gives rise to a Trait Significance measure TraitSignificance(i) = cor(x(i), TRAIT) where x(i) is the gene expression profile of the i th gene. Module Trait Significance = Average(Trait Significance values for genes in a module). CAMDA

15 Trait Significance Results Table shows average trait significance for each module. Every module was characterized in terms of a group of clinical traits. Interested in CFS severity trait CLUSTER because it contained the information from 14 clinical traits (evaluation responses). Focused on the green module (184 genes) since it was related to the CLUSTER trait. CAMDA

16 Analysis Overview 1. Construct gene co-expression network from microarray data. (Zhang and Horvath 2005) 2. Identify module of interest using trait data. 3. Determine informative SNP s and relate them to gene co-expression network. 4. Identify genes with statistical and biological significance. 5. Choose subset of CFS and control samples for validating the candidate biomarker. CAMDA

17 Finding SNPs associated with the CLUSTER trait We chose two SNPs with highest CLUSTER correlation. SNP12 = hcv on 12q21 (p-value = 0.01) SNP17 = hcv on 17q21 (p-value = 0.001) SNP & Cluster Correlation P-Values 8 7 hcv , 17q21 6 5q34 -Log(P-Value) hcv245410, 12q21 2p24 7p15 11p15 12q21 17q 22q X 1 0 SNP's Colored by Chromosome CAMDA

18 Correlation with relevant SNPs defines SNP Significance of the i th gene SNPSignificance = cor(x(i), SNP) (Where SNP data is additively coded). Conceptually related to a LOD* score at the SNP marker for the i th gene expression. Why correlate SNP and gene expression data? Puts SNP effect on the same footing as trait effect and gene-gene connection strengths. Effect sizes are important in our analysis. *LOD = logarithmic odds, a traditional measure of linkage between genetic loci. CAMDA

19 SNP Filtering & Significance Results Table shows the average SNP significance for each module. Green module genes most correlated with SNP12. SNP12 Sub-sample = average module correlations with SNP12 among samples that have a particular SNP12 and SNP17 genotype. Higher correlation(green module,snp12) in the sample subset. Module SNP Significance (Standard Error) SNPs Turquoise Grey Red Brown Green SNP (0.002) (0.001) (0.004) (0.004) (0.004) SNP (0.002) (0.001) (0.005) (0.003) 0.04 (0.002) SNP12 Sub-sample (0.005) (0.002) (0.009) (0.007) (0.007) CAMDA

20 Analysis Overview 1. Construct gene co-expression network from microarray data. (Zhang and Horvath 2005) 2. Identify module of interest using trait data. 3. Determine informative SNP s and relate them to gene co-expression network. 4. Identify genes with statistical and biological significance. 5. Choose subset of CFS and control samples for validating the candidate biomarker. CAMDA

21 Integration of genetic and network analysis Combined Gene Selection Criteria: 1. CLUSTER trait significance > SNP12 significance > Genes with high intramodular connectivity (top 50%). CAMDA

22 Eight Most Significant Genes: P-Value (Correlation) Accession Gene Symbol (Name) and Information Locus CLUSTER SNP Biomarker NM_ FOXN1 (forkhead box N1): Functions in defense response, T-cell immunodeficiency, and known to cause nudity in mice and humans. Expressed in thymus. 17q11-q (-0.21) (0.21) YES AF PRDX3 (peroxiredoxin 3): Regulates cell proliferation, differentiation, and antioxidant functions. 10q25-q (-0.26) 0.02 (0.21) YES AB PEX6 (peroxisomal biogenesis factor 6): absence results in zellweger syndrome (zws), neurological and metabolic defects. 6p (-0.23) (0.22) YES AF MYEF2 (myelin expression factor 2): myoblast cell differentiation and transcription. 15q (-0.21) (0.22) YES AF CRNKL1 (Crn, crooked neck-like 1 (Drosophila)): expressed in testes, involved in mrna splicing 20p (-0.27) (0.22) YES BC MED8 (mediator of RNA polymerase II transcription, subunit 8 homolog (yeast)): regulates transcription. 1p (-0.29) (0.22) YES XM_ Similar to polynucleotide phosphorylase-like protein and 3-5 RNA exonuclease (-0.29) (0.27) NO BC Unknown (protein for mgc:2780) (-0.23) (0.24) NO Source: NCBI ( CAMDA

23 FOXN1 Statistical Significance: Member of the green module that is related to the CFS severity trait (CLUSTER). High intramodular network connectivity. Significantly associated with SNP 12 (p-value = ), which is significantly associated with CLUSTER (p-value = 0.010). Moderate direct correlation with the CLUSTER trait. CAMDA

24 FOXN1 Biological Significance Mutations in mice & humans cause: Nudity. Depleted immune system due to dysfunctional T-cells. Highly expressed in thymus epithelia cells. Thymus involved in immune system: Converts lymphocytes to T-cells. Releases functional T-cells to combat infection. (Nehls et al. 1994; Pignata et al., 1996; Adriani et al. 2004) CAMDA

25 Ingenuity Pathway Analysis Cell Cycle Cellular Development Hair and Skin Development CAMDA

26 FOXN1: Validation for Chronic Fatigue Syndrome CFS patients have an overactive immune system & high T- cell production (Maher et al. 2005). FOXN1 may be highly expressed in CFS. But, how to further investigate this finding? There is a FOXN1 knockout mouse available. It would be relatively easy to explore the relationship between FOXN1 and fatigue in a mouse model. Photo source: CAMDA

27 Analysis Overview 1. Construct gene co-expression network from microarray data. (Zhang and Horvath 2005) 2. Identify module of interest using trait data. 3. Determine informative SNP s and relate them to gene co-expression network. 4. Identify genes with statistical and biological significance. 5. Choose subset of CFS and control samples for validating the candidate biomarker. CAMDA

28 Relationship between FOXN1 and SNP12 & 17 genotypes The two SNP s most correlated with the CLUSTER phenotype identify a sub-phenotype of CFS. SNP data is additively coded as 0,1,2. SNP rule: SNP 12 SNP 17 or We define a sample subgroup where all individuals have 0+2 or 1+2 genotypes. About 1/3 of the samples satisfy the SNP rule. For these samples FOXN1 is useful for predicting CFS severity. CAMDA

29 SNP Rule Aids in Patient Selection Green Brown Red Grey Turq SNP12 UNK UNK MED8 CRNKL1 MYEF2 PEX6 PRDX3 FOXN1 CLUSTER FOXN1 PRDX3 PEX6 MYEF2 CRNKL1 MED8 UNK UNK SNP12 Turquoise Grey Red Brown CLUSTER FOXN1 PRDX3 PEX6 MYEF2 CRNKL1 MED8 UNK UNK SNP12 Turquoise Grey Red Brown A. Correlations Among All Patients Color Key B. Correlations Among Patients Satisfying SNP Rule CAMDA

30 FOXN1 Expression Difference FOXN1 expression difference is most pronounced in SNP rule samples. All Samples SNP Rule Non SNP Rule ln(foxn1 Expression) Cases(74) Controls(41) Cases(16) Controls(17) Cases(58) Controls(24) We selected 13 cases and 15 controls from the samples satisfying the SNP rule. CAMDA

31 Summary 1. Constructed gene co-expression network from the microarray data. DNA Level: SNP Data mrna Level: Expression Data 3b. Related SNP data to Expression data SNP12 2. Related Expression data to CLUSTER trait GREEN 3a. Related SNP data to CLUSTER Trait SNP12, SNP17 Organism Level: Clinical Traits 4. FOXN1 has statistical and biological significance. 5. Highest differential FOXN1 expression in subgroup that has a particular SNP12 & 17 genotype. CAMDA

32 Acknowledgements Main Mentor: Steve Horvath, Biostatistics & Human Genetics Group Members Eric Sobel, Jeanette Papp, Jake Lusis Mouse genetics Jake Lusis, Sud Doss, Anatole Ghazalpour Human/chimp brain Mike Oldham, Dan Geschwind CAMDA

33 References Adriani, M., Martinez-Mir, A., Fusco, F., Busiello, R., Frank, J., Telese, S., Matrecano, E., Ursini, M.V., Christiano, A.M., Pignata, C. (2004). Ann Hum Genet 68, Maher, K. J., Klimas, N. G., Fletcher, M. A. (2005) Clin Exp Immunol 142, Nehls, M., Pfeifer, D., Schorpp, M., Hedrich, H., and Boehm, T. (1994). Nature 372, Pignata, C., Fiore, M., Guzzetta, V., Castaldo, A., Sebastio, G., Porta, F., and Guarino, A. (1996). Am J Med Genet 65, Zhang, B. and Horvath, S. (2005). Statistical Applications in Genetics and Molecular Biology 4, 17. CAMDA