Chung-Feng Kao

A comprehensive network and pathway analysis of candidate genes in major depressive disorder

BackgroundNumerous genetic and genomic datasets related to complex diseases have been made available during the last decade. It is now a great challenge to assess such heterogeneous datasets to prioritize disease genes and perform follow up functional analysis and validation. Among complex disease studies, psychiatric disorders such as major depressive disorder (MDD) are especially in need of robust integrative analysis because these diseases are more complex than others, with weak genetic factors at various levels, including genetic markers, transcription (gene expression), epigenetics (methylation), protein, pathways and networks.ResultsIn this study, we proposed a comprehensive analysis framework at the systems level and demonstrated it in MDD using a set of candidate genes that have recently been prioritized based on multiple lines of evidence including association, linkage, gene expression (both human and animal studies), regulatory pathway, and literature search. In the network analysis, we explored the topological characteristics of these genes in the context of the human interactome and compared them with two other complex diseases. The network topological features indicated that MDD is similar to schizophrenia compared to cancer. In the functional analysis, we performed the gene set enrichment analysis for both Gene Ontology categories and canonical pathways. Moreover, we proposed a unique pathway crosstalk approach to examine the dynamic interactions among biological pathways. Our pathway enrichment and crosstalk analyses revealed two unique pathway interaction modules that were significantly enriched with MDD genes. These two modules are neuro-transmission and immune system related, supporting the neuropathology hypothesis of MDD. Finally, we constructed a MDD-specific subnetwork, which recruited novel candidate genes with association signals from a major MDD GWAS dataset.ConclusionsThis study is the first systematic network and pathway analysis of candidate genes in MDD, providing abundant important information about gene interaction and regulation in a major psychiatric disease. The results suggest potential functional components underlying the molecular mechanisms of MDD and, thus, facilitate generation of novel hypotheses in this disease. The systems biology based strategy in this study can be applied to many other complex diseases.

Prioritization and Evaluation of Depression Candidate Genes by Combining Multidimensional Data Resources

Background Large scale and individual genetic studies have suggested numerous susceptible genes for depression in the past decade without conclusive results. There is a strong need to review and integrate multi-dimensional data for follow up validation. The present study aimed to apply prioritization procedures to build-up an evidence-based candidate genes dataset for depression. Methods Depression candidate genes were collected in human and animal studies across various data resources. Each gene was scored according to its magnitude of evidence related to depression and was multiplied by a source-specific weight to form a combined score measure. All genes were evaluated through a prioritization system to obtain an optimal weight matrix to rank their relative importance with depression using the combined scores. The resulting candidate gene list for depression (DEPgenes) was further evaluated by a genome-wide association (GWA) dataset and microarray gene expression in human tissues. Results A total of 5,055 candidate genes (4,850 genes from human and 387 genes from animal studies with 182 being overlapped) were included from seven data sources. Through the prioritization procedures, we identified 169 DEPgenes, which exhibited high chance to be associated with depression in GWA dataset (Wilcoxon rank-sum test, p = 0.00005). Additionally, the DEPgenes had a higher percentage to express in human brain or nerve related tissues than non-DEPgenes, supporting the neurotransmitter and neuroplasticity theories in depression. Conclusions With comprehensive data collection and curation and an application of integrative approach, we successfully generated DEPgenes through an effective gene prioritization system. The prioritized DEPgenes are promising for future biological experiments or replication efforts to discoverthe underlying molecular mechanisms for depression.

Enriched pathways for major depressive disorder identified from a genome-wide association study

Major depressive disorder (MDD) has caused a substantial burden of disease worldwide with moderate heritability. Despite efforts through conducting numerous association studies and now, genome-wide association (GWA) studies, the success of identifying susceptibility loci for MDD has been limited, which is partially attributed to the complex nature of depression pathogenesis. A pathway-based analytic strategy to investigate the joint effects of various genes within specific biological pathways has emerged as a powerful tool for complex traits. The present study aimed to identify enriched pathways for depression using a GWA dataset for MDD. For each gene, we estimated its gene-wise p value using combined and minimum p value, separately. Canonical pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioCarta were used. We employed four pathway-based analytic approaches (gene set enrichment analysis, hypergeometric test, sum-square statistic, sum-statistic). We adjusted for multiple testing using Benjamini & Hochbergs method to report significant pathways. We found 17 significantly enriched pathways for depression, which presented low-to-intermediate crosstalk. The top four pathways were long-term depression (p⩽1×10-5), calcium signalling (p⩽6×10-5), arrhythmogenic right ventricular cardiomyopathy (p⩽1.6×10-4) and cell adhesion molecules (p⩽2.2×10-4). In conclusion, our comprehensive pathway analyses identified promising pathways for depression that are related to neurotransmitter and neuronal systems, immune system and inflammatory response, which may be involved in the pathophysiological mechanisms underlying depression. We demonstrated that pathway enrichment analysis is promising to facilitate our understanding of complex traits through a deeper interpretation of GWA data. Application of this comprehensive analytic strategy in upcoming GWA data for depression could validate the findings reported in this study.

Physical mapping of RFLP markers on four chromosome arms in maize using terminal deficiencies.

Abstract Terminal deficiencies (TDs) generated by the r-XI deletion system in maize were used to physically map RFLP markers on the short arm of chromosome 2 (2S) and the long arm of chromosome 6 (6L), chromosome 8 (8L), and chromosome 10 (10L). Five TDs on 2S, 8 on 6L, 10 on 8L, and 20 on 10L were isolated using the recessive morphological markers lg1, py1, j1(gl18), and sr2, respectively, for selection. Two exceptional TDs on 2S and 8L also have a second breakpoint on the long arm of chromosome 2 (2L) and 8L, respectively. The physical mapping of RFLP probes in relation to TD breakpoints was done by Southern hybridization. The five TDs on 2S divide chromosome 2 into four regions, all of which are distinguishable by RFLP markers. Likewise, three remaining chromosome arms are divided by TDs into RFLP-marked regions: 8 TDs divide 6L into five regions, 10 TDs divided 8L into seven regions, and 20 TDs divide 10L into three regions. The linear order of the physical map of 6L and 8L is consistent with that of the genetic maps, but that of 2L and 10L is not. Four groups of markers on 2S as well as 2L, and two on 10L are in reverse order in the physical map compared with the genetic maps. Other intriguing results are that breakpoints of TDs on 6L and 8L are distributed throughout the selected region, but most of those on 2L and 10L cluster in a region near the centromere; a single TD arose after fertilization.

Pathway Analysis Using Information from Allele-Specific Gene Methylation in Genome-Wide Association Studies for Bipolar Disorder

Bipolar disorder (BPD) is a complex psychiatric trait with high heritability. Despite efforts through conducting genome-wide association (GWA) studies, the success of identifying susceptibility loci for BPD has been limited, which is partially attributed to the complex nature of its pathogenesis. Pathway-based analytic strategy is a powerful tool to explore joint effects of gene sets within specific biological pathways. Additionally, to incorporate other aspects of genomic data into pathway analysis may further enhance our understanding for the underlying mechanisms for BPD. Patterns of DNA methylation play important roles in regulating gene expression and function. A commonly observed phenomenon, allele-specific methylation (ASM) describes the associations between genetic variants and DNA methylation patterns. The present study aimed to identify biological pathways that are involve in the pathogenesis of BPD while incorporating brain specific ASM information in pathway analysis using two large-scale GWA datasets in Caucasian populations. A weighting scheme was adopted to take ASM information into consideration for each pathway. After multiple testing corrections, we identified 88 and 15 enriched pathways for their biological relevance for BPD in the Genetic Association Information Network (GAIN) and the Wellcome Trust Case Control Consortium dataset, respectively. Many of these pathways were significant only when applying the weighting scheme. Three ion channel related pathways were consistently identified in both datasets. Results in the GAIN dataset also suggest for the roles of extracellular matrix in brain for BPD. Findings from Gene Ontology (GO) analysis exhibited functional enrichment among genes of non-GO pathways in activity of gated channel, transporter, and neurotransmitter receptor. We demonstrated that integrating different data sources with pathway analysis provides an avenue to identify promising and novel biological pathways for exploring the underlying molecular mechanisms for bipolar disorder. Further basic research can be conducted to target the biological mechanisms for the identified genes and pathways.

Association and interaction of APOA5 , BUD13 , CETP , LIPA and health-related behavior with metabolic syndrome in a Taiwanese population

Increased risk of developing metabolic syndrome (MetS) has been associated with the APOA5, APOC1, BRAP, BUD13, CETP, LIPA, LPL, PLCG1, and ZPR1 genes. In this replication study, we reassessed whether these genes are associated with MetS and its individual components independently and/or through complex interactions in a Taiwanese population. We also analyzed the interactions between environmental factors and these genes in influencing MetS and its individual components. A total of 3,000 Taiwanese subjects were assessed in this study. Metabolic traits such as waist circumference, triglyceride, high-density lipoprotein (HDL) cholesterol, systolic and diastolic blood pressure, and fasting glucose were measured. Our data showed a nominal association of MetS with the APOA5 rs662799, BUD13 rs11216129, BUD13 rs623908, CETP rs820299, and LIPA rs1412444 single nucleotide polymorphisms (SNPs). Moreover, APOA5 rs662799, BUD13 rs11216129, and BUD13 rs623908 were significantly associated with high triglyceride, low HDL, triglyceride, and HDL levels. Additionally, we found the interactions of APOA5 rs662799, BUD13 rs11216129, BUD13 rs623908, CETP rs820299, LIPA rs1412444, alcohol consumption, smoking status, or physical activity on MetS and its individual components. Our study indicates that the APOA5, BUD13, CETP, and LIPA genes may contribute to the risk of MetS independently as well as through gene-gene and gene-environment interactions.

Incorporating information of microRNAs into pathway analysis in a genome-wide association study of bipolar disorder

MicroRNAs (miRNAs) are known to be important post-transcriptional regulators that are involved in the etiology of complex psychiatric traits. The present study aimed to incorporate miRNAs information into pathway analysis using a genome-wide association dataset to identify relevant biological pathways for bipolar disorder (BPD). We selected psychiatric- and neurological-associated miRNAs (N = 157) from PhenomiR database. The miRNA target genes (miTG) predictions were obtained from microRNA.org. Canonical pathways (N = 4,051) were downloaded from the Molecule Signature Database. We employed a novel weighting scheme for miTGs in pathway analysis using methods of gene set enrichment analysis and sum-statistic. Under four statistical scenarios, 38 significantly enriched pathways (P-value < 0.01 after multiple testing correction) were identified for the risk of developing BPD, including pathways of ion channels associated (e.g., gated channel activity, ion transmembrane transporter activity, and ion channel activity) and nervous related biological processes (e.g., nervous system development, cytoskeleton, and neuroactive ligand receptor interaction). Among them, 19 were identified only when the weighting scheme was applied. Many miRNA-targeted genes were functionally related to ion channels, collagen, and axonal growth and guidance that have been suggested to be associated with BPD previously. Some of these genes are linked to the regulation of miRNA machinery in the literature. Our findings provide support for the potential involvement of miRNAs in the psychopathology of BPD. Further investigations to elucidate the functions and mechanisms of identified candidate pathways are needed.

Explore the Features of Brain-Derived Neurotrophic Factor in Mood Disorders

Objectives Brain-derived neurotrophic factor (BDNF) plays important roles in neuronal survival and differentiation; however, the effects of BDNF on mood disorders remain unclear. We investigated BDNF from the perspective of various aspects of systems biology, including its molecular evolution, genomic studies, protein functions, and pathway analysis. Methods We conducted analyses examining sequences, multiple alignments, phylogenetic trees and positive selection across 12 species and several human populations. We summarized the results of previous genomic and functional studies of pro-BDNF and mature-BDNF (m-BDNF) found in a literature review. We identified proteins that interact with BDNF and performed pathway-based analysis using large genome-wide association (GWA) datasets obtained for mood disorders. Results BDNF is encoded by a highly conserved gene. The chordate BDNF genes exhibit an average of 75% identity with the human gene, while vertebrate orthologues are 85.9%-100% identical to human BDNF. No signs of recent positive selection were found. Associations between BDNF and mood disorders were not significant in most of the genomic studies (e.g., linkage, association, gene expression, GWA), while relationships between serum/plasma BDNF level and mood disorders were consistently reported. Pro-BDNF is important in the response to stress; the literature review suggests the necessity of studying both pro- and m-BDNF with regard to mood disorders. In addition to conventional pathway analysis, we further considered proteins that interact with BDNF (I-Genes) and identified several biological pathways involved with BDNF or I-Genes to be significantly associated with mood disorders. Conclusions Systematically examining the features and biological pathways of BDNF may provide opportunities to deepen our understanding of the mechanisms underlying mood disorders.

Association and interaction effects of Alzheimer’s disease-associated genes and lifestyle on cognitive aging in older adults in a Taiwanese population

Genome-wide association studies and meta-analyses implicated that increased risk of developing Alzheimers diseases (AD) has been associated with the ABCA7, APOE, BIN1, CASS4, CD2AP, CD33, CELF1, CLU, CR1, DSG2, EPHA1, FERMT2, HLA-DRB1, HLA-DRB4, INPP5D, MEF2C, MS4A4A, MS4A4E, MS4A6E, NME8, PICALM, PLD3, PTK2B, RIN3, SLC24A4, SORL1, and ZCWPW1 genes. In this study, we assessed whether single nucleotide polymorphisms (SNPs) within these 27 AD-associatedgenes are linked with cognitive aging independently and/or through complex interactions in an older Taiwanese population. We also analyzed the interactions between lifestyle and these genes in influencing cognitive aging. A total of 634 Taiwanese subjects aged over 60 years from the Taiwan Biobank were analyzed. Mini-Mental State Examination (MMSE) scores were performed for all subjects to evaluate cognitive functions. Out of the 588 SNPs tested in this study, only the association between CASS4-rs911159 and cognitive aging persisted significantly (P = 2.2 × 10−5) after Bonferroni correction. Our data also showed a nominal association of cognitive aging with the SNPs in six more key AD-associated genes, including EPHA1-rs10952552, FERMT2-rs4901317, MEF2C-rs9293506, PLD3-rs11672825, RIN3-rs1885747, and SLC24A4-rs67063100 (P = 0.0018∼0.0097). Additionally, we found the interactions among CASS4-rs911159, EPHA-rs10952552, FERMT2-rs4901317, MEF2C-rs9293506, or SLC24A4-rs67063100 on cognitive aging (P = 0.004∼0.035). Moreover, our analysis suggested the interactions of SLC24A4-rs67063100 or MEF2C-rs9293506 with lifestyle such as alcohol consumption, smoking status, physical activity, or social support on cognitive aging (P = 0.008∼0.041). Our study indicates that the AD-associated genes may contribute to the risk of cognitive aging independently as well as through gene-gene and gene-lifestyle interactions.

The ADAMTS9 gene is associated with cognitive aging in the elderly in a Taiwanese population

Evidence indicates that the pathophysiologic mechanisms associated with insulin resistance may contribute to cognitive aging and Alzheimer’s diseases. In this study, we hypothesize that single nucleotide polymorphisms (SNPs) within insulin resistance-associated genes, such as the ADAM metallopeptidase with thrombospondin type 1 motif 9 (ADAMTS9), glucokinase regulator (GCKR), and peroxisome proliferator activated receptor gamma (PPARG) genes, may be linked with cognitive aging independently and/or through complex interactions in an older Taiwanese population. A total of 547 Taiwanese subjects aged over 60 years from the Taiwan Biobank were analyzed. Mini-Mental State Examinations (MMSE) were administered to all subjects, and MMSE scores were used to measure cognitive functions. Our data showed that four SNPs (rs73832338, rs9985304, rs4317088, and rs9831846) in the ADAMTS9 gene were significantly associated with cognitive aging among the subjects (P = 1.5 x 10−6 ~ 0.0002). This association remained significant after performing Bonferroni correction. Additionally, we found that interactions between the ADAMTS9 rs9985304 and ADAMTS9 rs76346246 SNPs influenced cognitive aging (P < 0.001). However, variants in the GCKR and PPARG genes had no association with cognitive aging in our study. Our study indicates that the ADAMTS9 gene may contribute to susceptibility to cognitive aging independently as well as through SNP-SNP interactions.