title: Network-Based Meta-Analyses of Associations of Multiple Gene Expression Profiles with Bone Mineral Density Variations in Women.
abstract: BACKGROUND
  Existing microarray studies of bone mineral density (BMD) have been critical for understanding the pathophysiology of osteoporosis, and have identified a number of candidate genes. However, these studies were limited by their relatively small sample sizes and were usually analyzed individually. Here, we propose a novel network-based meta-analysis approach that combines data across six microarray studies to identify functional modules from human protein-protein interaction (PPI) data, and highlight several differentially expressed genes (DEGs) and a functional module that may play an important role in BMD regulation in women.
  
  
  METHODS
  Expression profiling studies were identified by searching PubMed, Gene Expression Omnibus (GEO) and ArrayExpress. Two meta-analysis methods were applied across different gene expression profiling studies. The first, a nonparametric Fisher's method, combined p-values from individual experiments to identify genes with large effect sizes. The second method combined effect sizes from individual datasets into a meta-effect size to gain a higher precision of effect size estimation across all datasets. Genes with Q test's p-values < 0.05 or I(2) values > 50% were assessed by a random effects model and the remainder by a fixed effects model. Using Fisher's combined p-values, functional modules were identified through an integrated analysis of microarray data in the context of large protein-protein interaction (PPI) networks. Two previously published meta-analysis studies of genome-wide association (GWA) datasets were used to determine whether these module genes were genetically associated with BMD. Pathway enrichment analysis was performed with a hypergeometric test.
  
  
  RESULTS
  Six gene expression datasets were identified, which included a total of 249 (129 high BMD and 120 low BMD) female subjects. Using a network-based meta-analysis, a consensus module containing 58 genes (nodes) and 83 edges was detected. Pathway enrichment analysis of the 58 module genes revealed that these genes were enriched in several important KEGG pathways including Osteoclast differentiation, B cell receptor signaling pathway, MAPK signaling pathway, Chemokine signaling pathway and Insulin signaling pathway. The importance of module genes was replicated by demonstrating that most module genes were genetically associated with BMD in the GWAS data sets. Meta-analyses were performed at the individual gene level by combining p-values and effect sizes. Five candidate genes (ESR1, MAP3K3, PYGM, RAC1 and SYK) were identified based on gene expression meta-analysis, and their associations with BMD were also replicated by two BMD meta-analysis studies.
  
  
  CONCLUSIONS
  In summary, our network-based meta-analysis not only identified important differentially expressed genes but also discovered biologically meaningful functional modules for BMD determination. Our study may provide novel therapeutic targets for osteoporosis in women.
authors.affiliation: Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America.
authors.forename: Hao
authors.initials: H
authors.lastname: He
authors.affiliation: Department of Biomedical Engineering, Tulane University, New Orleans, Louisiana, United States of America.
authors.forename: Shaolong
authors.initials: S
authors.lastname: Cao
authors.affiliation: Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America.
authors.forename: Tianhua
authors.initials: T
authors.lastname: Niu
authors.affiliation: Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America.
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America.
authors.forename: Lan
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America.
authors.forename: Yong
authors.initials: Y
authors.lastname: Zeng
authors.affiliation: Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhu
authors.affiliation: Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America.|Department of Biomedical Engineering, Tulane University, New Orleans, Louisiana, United States of America.
authors.forename: Yu-ping
authors.initials: YP
authors.lastname: Wang
authors.affiliation: Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America.
authors.forename: Hong-wen
authors.initials: HW
authors.lastname: Deng
mesh_terms: D015519:Bone Density / Q000235:genetics*; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D010024:Osteoporosis / Q000235:genetics*