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1.  A Conserved BDNF, Glutamate- and GABA-Enriched Gene Module Related to Human Depression Identified by Coexpression Meta-Analysis and DNA Variant Genome-Wide Association Studies 
PLoS ONE  2014;9(3):e90980.
Large scale gene expression (transcriptome) analysis and genome-wide association studies (GWAS) for single nucleotide polymorphisms have generated a considerable amount of gene- and disease-related information, but heterogeneity and various sources of noise have limited the discovery of disease mechanisms. As systematic dataset integration is becoming essential, we developed methods and performed meta-clustering of gene coexpression links in 11 transcriptome studies from postmortem brains of human subjects with major depressive disorder (MDD) and non-psychiatric control subjects. We next sought enrichment in the top 50 meta-analyzed coexpression modules for genes otherwise identified by GWAS for various sets of disorders. One coexpression module of 88 genes was consistently and significantly associated with GWAS for MDD, other neuropsychiatric disorders and brain functions, and for medical illnesses with elevated clinical risk of depression, but not for other diseases. In support of the superior discriminative power of this novel approach, we observed no significant enrichment for GWAS-related genes in coexpression modules extracted from single studies or in meta-modules using gene expression data from non-psychiatric control subjects. Genes in the identified module encode proteins implicated in neuronal signaling and structure, including glutamate metabotropic receptors (GRM1, GRM7), GABA receptors (GABRA2, GABRA4), and neurotrophic and development-related proteins [BDNF, reelin (RELN), Ephrin receptors (EPHA3, EPHA5)]. These results are consistent with the current understanding of molecular mechanisms of MDD and provide a set of putative interacting molecular partners, potentially reflecting components of a functional module across cells and biological pathways that are synchronously recruited in MDD, other brain disorders and MDD-related illnesses. Collectively, this study demonstrates the importance of integrating transcriptome data, gene coexpression modules and GWAS results for providing novel and complementary approaches to investigate the molecular pathology of MDD and other complex brain disorders.
PMCID: PMC3946570  PMID: 24608543
2.  Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline 
BMC Bioinformatics  2013;14:368.
As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have become routine in biomedical research. In this paper, we focus on microarray meta-analysis, where multiple microarray studies with relevant biological hypotheses are combined in order to improve candidate marker detection. Many methods have been developed and applied in the literature, but their performance and properties have only been minimally investigated. There is currently no clear conclusion or guideline as to the proper choice of a meta-analysis method given an application; the decision essentially requires both statistical and biological considerations.
We performed 12 microarray meta-analysis methods for combining multiple simulated expression profiles, and such methods can be categorized for different hypothesis setting purposes: (1) HS A : DE genes with non-zero effect sizes in all studies, (2) HS B : DE genes with non-zero effect sizes in one or more studies and (3) HS r : DE gene with non-zero effect in "majority" of studies. We then performed a comprehensive comparative analysis through six large-scale real applications using four quantitative statistical evaluation criteria: detection capability, biological association, stability and robustness. We elucidated hypothesis settings behind the methods and further apply multi-dimensional scaling (MDS) and an entropy measure to characterize the meta-analysis methods and data structure, respectively.
The aggregated results from the simulation study categorized the 12 methods into three hypothesis settings (HS A , HS B , and HS r ). Evaluation in real data and results from MDS and entropy analyses provided an insightful and practical guideline to the choice of the most suitable method in a given application. All source files for simulation and real data are available on the author’s publication website.
PMCID: PMC3898528  PMID: 24359104
3.  An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection 
Bioinformatics  2012;28(19):2534-2536.
Summary: With the rapid advances and prevalence of high-throughput genomic technologies, integrating information of multiple relevant genomic studies has brought new challenges. Microarray meta-analysis has become a frequently used tool in biomedical research. Little effort, however, has been made to develop a systematic pipeline and user-friendly software. In this article, we present MetaOmics, a suite of three R packages MetaQC, MetaDE and MetaPath, for quality control, differentially expressed gene identification and enriched pathway detection for microarray meta-analysis. MetaQC provides a quantitative and objective tool to assist study inclusion/exclusion criteria for meta-analysis. MetaDE and MetaPath were developed for candidate marker and pathway detection, which provide choices of marker detection, meta-analysis and pathway analysis methods. The system allows flexible input of experimental data, clinical outcome (case–control, multi-class, continuous or survival) and pathway databases. It allows missing values in experimental data and utilizes multi-core parallel computing for fast implementation. It generates informative summary output and visualization plots, operates on different operation systems and can be expanded to include new algorithms or combine different types of genomic data. This software suite provides a comprehensive tool to conveniently implement and compare various genomic meta-analysis pipelines.
Supplementary Information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3463115  PMID: 22863766
4.  The Role of Genetic Sex in Affect Regulation and Expression of GABA-Related Genes Across Species 
Although circulating hormones and inhibitory gamma-aminobutyric acid (GABA)-related factors are known to affect mood, considerable knowledge gaps persist for biological mechanisms underlying the female bias in mood disorders. Here, we combine human and mouse studies to investigate sexual dimorphism in the GABA system in the context of major depressive disorder (MDD) and then use a genetic model to dissect the role of sex-related factors in GABA-related gene expression and anxiety-/depressive-like behaviors in mice. First, using meta-analysis of gene array data in human postmortem brain (N = 51 MDD subjects, 50 controls), we show that the previously reported down-regulation in MDD of somatostatin (SST), a marker of a GABA neuron subtype, is significantly greater in women with MDD. Second, using gene co-expression network analysis in control human subjects (N = 214; two frontal cortex regions) and expression quantitative trait loci mapping (N = 170 subjects), we show that expression of SST and the GABA-synthesizing enzymes glutamate decarboxylase 67 (GAD67) and GAD65 are tightly co-regulated and influenced by X-chromosome genetic polymorphisms. Third, using a rodent genetic model [Four Core Genotypes (FCG) mice], in which genetic and gonadal sex are artificially dissociated (N ≥ 12/group), we show that genetic sex (i.e., X/Y-chromosome) influences both gene expression (lower Sst, Gad67, Gad65 in XY mice) and anxiety-like behaviors (higher in XY mice). This suggests that in an intact male animal, the observed behavior represents the outcomes of male genetic sex increasing and male-like testosterone decreasing anxiety-like behaviors. Gonadal sex was the only factor influencing depressive-like behavior (gonadal males < gonadal females). Collectively, these combined human and mouse studies provide mechanistic insight into sexual dimorphism in mood disorders, and specifically demonstrate an unexpected role of male-like factors (XY genetic sex) on GABA-related genes and anxiety-like behaviors.
PMCID: PMC3775314  PMID: 24062698
GABA; genetic sex; mood; somatostatin; anxiety; depression
5.  A Genome-Wide Homozygosity Association Study Identifies Runs of Homozygosity Associated with Rheumatoid Arthritis in the Human Major Histocompatibility Complex 
PLoS ONE  2012;7(4):e34840.
Rheumatoid arthritis (RA) is a chronic inflammatory disorder with a polygenic mode of inheritance. This study examined the hypothesis that runs of homozygosity (ROHs) play a recessive-acting role in the underlying RA genetic mechanism and identified RA-associated ROHs. Ours is the first genome-wide homozygosity association study for RA and characterized the ROH patterns associated with RA in the genomes of 2,000 RA patients and 3,000 normal controls of the Wellcome Trust Case Control Consortium. Genome scans consistently pinpointed two regions within the human major histocompatibility complex region containing RA-associated ROHs. The first region is from 32,451,664 bp to 32,846,093 bp (−log10(p)>22.6591). RA-susceptibility genes, such as HLA-DRB1, are contained in this region. The second region ranges from 32,933,485 bp to 33,585,118 bp (−log10(p)>8.3644) and contains other HLA-DPA1 and HLA-DPB1 genes. These two regions are physically close but are located in different blocks of linkage disequilibrium, and ∼40% of the RA patients' genomes carry these ROHs in the two regions. By analyzing homozygote intensities, an ROH that is anchored by the single nucleotide polymorphism rs2027852 and flanked by HLA-DRB6 and HLA-DRB1 was found associated with increased risk for RA. The presence of this risky ROH provides a 62% accuracy to predict RA disease status. An independent genomic dataset from 868 RA patients and 1,194 control subjects of the North American Rheumatoid Arthritis Consortium successfully validated the results obtained using the Wellcome Trust Case Control Consortium data. In conclusion, this genome-wide homozygosity association study provides an alternative to allelic association mapping for the identification of recessive variants responsible for RA. The identified RA-associated ROHs uncover recessive components and missing heritability associated with RA and other autoimmune diseases.
PMCID: PMC3335047  PMID: 22536334

Results 1-5 (5)