* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Overview of Weighted Gene Co- Expression Network Analysis
Fetal origins hypothesis wikipedia , lookup
X-inactivation wikipedia , lookup
Epigenetics in stem-cell differentiation wikipedia , lookup
Point mutation wikipedia , lookup
Oncogenomics wikipedia , lookup
Metabolic network modelling wikipedia , lookup
Genomic imprinting wikipedia , lookup
Pathogenomics wikipedia , lookup
Epigenetics of neurodegenerative diseases wikipedia , lookup
History of genetic engineering wikipedia , lookup
Genetic engineering wikipedia , lookup
Copy-number variation wikipedia , lookup
Epigenetics of human development wikipedia , lookup
Epigenetics in learning and memory wikipedia , lookup
Genome evolution wikipedia , lookup
Public health genomics wikipedia , lookup
Genome (book) wikipedia , lookup
Vectors in gene therapy wikipedia , lookup
Neuronal ceroid lipofuscinosis wikipedia , lookup
Saethre–Chotzen syndrome wikipedia , lookup
Gene therapy of the human retina wikipedia , lookup
The Selfish Gene wikipedia , lookup
Helitron (biology) wikipedia , lookup
Epigenetics of diabetes Type 2 wikipedia , lookup
Gene therapy wikipedia , lookup
Gene desert wikipedia , lookup
Nutriepigenomics wikipedia , lookup
Site-specific recombinase technology wikipedia , lookup
Gene expression programming wikipedia , lookup
Therapeutic gene modulation wikipedia , lookup
Gene nomenclature wikipedia , lookup
Gene expression profiling wikipedia , lookup
Microevolution wikipedia , lookup
Overview of Weighted Gene CoExpression Network Analysis (WGCNA) Steve Horvath & Brian Chen Human Genetics and Biostatistics UCLA http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ Networks are particularly valuable for data integration • Resulting analysis is known as – – – “systems biology“ “systems genetics” “integromics” • WGCNA useful for correlating disparate data sets: – – – – SNPs Gene expression DNA methylation Clinical outcomes Nature (2011) 25;474(7351):380-4 PNAS (2010) 107(28):12698-703 PloS Genetics (2009) ;5(9):e1000642 Nature (2008) Mar 27;452(7186):429-35 Standard analyses identify “differentially expressed” or “differentially methylated” genes Control Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 Gene 7 Gene 8 Gene 9 Gene 10 Gene 11 Gene 12 Gene 13 Gene 14 Gene 15 Gene 16 . . . . . . . . . . . Gene n-2 Gene n-1 Gene n Experimental • Each gene is treated as an individual entity. • • • Misses the forest for the trees Ignores the strong correlations between genes Plagued by false positives due to multiple comparisons Comparison of gene-centric vs. network approaches Gene-centric Network Number of comparisons ~105 ~101 Reproducibility • Results are sensitive to analytic decisions • Robust statistical framework • focus on pathways Pathway information Data bases Data-driven + data-bases Typical analysis steps of weighted correlation network analysis (WGCNA) Construct a network Rationale: make use of interaction patterns between genes Identify modules Rationale: module (pathway) based analysis Relate modules to external information Array Information: Clinical traits, SNPs, proteomics Gene Information: gene ontology, pathways, enrichment Rationale: find biologically interesting modules Study Module Preservation across data Rationale: • Same data: to check robustness of module definition • Different data: to find interesting modules. Find the key drivers in interesting modules Tools: intramodular connectivity, causality testing Rationale: experimental validation, therapeutics, biomarkers Constructing co-expression networks High dimensional data (e.g. expression, methylation) Measure of co-expression Hierarchical clustering to identify modules (clusters) Network Data reduction with networks Relative risk for CVD Hub gene = most highly connected gene Module Eigengene = weighted average Connectivity Relating modules to clinical traits: Table with correlations and p-values Modules Clinical traits related to metabolic syndrome (data by AJ Lusis) WGCNA in WHI Long Life Study High-dimensional Data • RNA • methylation • metabolites WCGNA data integration & reduction Modules Clinical Traits • CVD • Blood counts • Glucose • Insulin • CRP • Creatinine • Triglycerides • Cholesterol • Blood pressure • Height/weight • Waist • Physical performance • Physical activity Gene Ontology, Pathways, Enrichment Ingenuity Pathway Analysis (IPA) Gene-Set Enrichment Analysis (GSEA) Gene Ontology (DAVID/EASE) Systems Genetics Integration (“Integromics”) SNP • eQTL • Causal pathway testing • SNP-set enrichment Gene expression • GWAS • Module eigengenes • SNP-set enrichment Clinical traits Conclusions • WGCNA is a highly robust, systems approach for: • Integrating high-dimensional, multi-scale data • • • • • Microarray RNA-seq Methylation Proteomics MRI • Identify modules and key driver genes that relate to disease outcomes