Hongchao Lv

Meta-Analysis of 125 Rheumatoid Arthritis-Related Single Nucleotide Polymorphisms Studied in the Past Two Decades

Objective Candidate gene association studies and genome-wide association studies (GWAs) have identified a large number of single nucleotide polymorphisms (SNPs) loci affecting susceptibility to rheumatoid arthritis (RA). However, for the same locus, some studies have yielded inconsistent results. To assess all the available evidence for association, we performed a meta-analysis on previously published case-control studies investigating the association between SNPs and RA. Methods Two hundred and sixteen studies, involving 125 SNPs, were reviewed. For each SNP, three genetic models were considered: the allele, dominant and recessive effects models. For each model, the effect summary odds ratio (OR) and 95% CIs were calculated. Cochran’s Q-statistics were used to assess heterogeneity. If the heterogeneity was high, a random effects model was used for meta-analysis, otherwise a fixed effects model was used. Results The meta-analysis results showed that: (1) 30, 28 and 26 SNPs were significantly associated with RA (P<0.01) for the allele, dominant, and recessive models, respectively. (2) rs2476601 (PTPN22) showed the strongest association for all the three models: OR = 1.605, 95% CI: 1.540–1.672, P<1.00E−15 for the T-allele; OR = 1.638, 95% CI: 1.565–1.714, P<1.00E−15 for the T/T+T/C genotype and OR = 2.544, 95% CI: 2.173–2.978, P<1.00E−15 for the T/T genotype. (3) Only 23 (18.4%), 13 (10.4%) and 15 (12.0%) SNPs had high heterogeneity (P<0.01) for the three models, respectively. (4) For some of the SNPs, there was no publication bias according to Funnel plots and Egger’s regression tests (P<0.01). For the other SNPs, the associations were tested in only a few studies, and may have been subject to publication bias. More studies on these loci are required. Conclusion Our meta-analysis provides a comprehensive evaluation of the RA association studies from the past two decades. The detailed meta-analysis results are available at: http://210.46.85.180/DRAP/index.php/Metaanalysis/index.

The expanded human disease network combining protein–protein interaction information

The human disease network (HDN) has become a powerful tool for revealing disease–disease associations. Some studies have shown that genes that share similar or same disease phenotypes tend to encode proteins that interact with each other. Therefore, protein–protein interactions (PPIs) may help us to further understand the relationships between diseases with overlapping clinical phenotypes. In this study, we constructed the expanded HDN (eHDN) by combining disease gene information with PPI information, and analyzed its topological features and functional properties. We found that the network is hierarchical and, most diseases are connected to only a few diseases, whereas a small part of diseases are linked to many different diseases. Diseases in a specific disease class tend to cluster together, and genes associated with the same disease are functionally related. Comparing the eHDN with the original HDN (oHDN, constructed using disease gene information) revealed high consistency over all topological and functional properties. This, to some extent, indicates that our eHDN is reliable. In the eHDN, we found some new associations among diseases resulting from the shared genes interacting with disease genes. The new eHDN will provide a valuable reference for clinicians and medical researchers.

Prioritization of Candidate Genes for Periodontitis Using Multiple Computational Tools

BACKGROUND Both genetic and environmental factors contribute to the development of periodontitis. Genetic studies identified a variety of candidate genes for periodontitis. The aim of the present study is to identify the most promising candidate genes for periodontitis using an integrative gene ranking method. METHODS Seed genes that were confirmed to be associated with periodontitis were identified using text mining. Three types of candidate genes were then extracted from different resources (expression profiles, genome-wide association studies). Combining the seed genes, four freely available bioinformatics tools (ToppGene, DIR, Endeavour, and GPEC) were integrated for prioritization of candidate genes. Candidate genes that identified with at least three programs and ranked in the top 20 by each program were considered the most promising. RESULTS Prioritization analysis resulted in 21 promising genes involved or potentially involved in periodontitis. Among them, IL18 (interleukin 18), CD44 (CD44 molecule), CXCL1 (chemokine [CXC motif] ligand 1), IL6ST (interleukin 6 signal transducer), MMP3 (matrix metallopeptidase 3), MMP7, CCR1 (chemokine [C-C motif] receptor 1), MMP13, and TLR9 (Toll-like receptor 9) had been associated with periodontitis. However, the roles of other genes, such as CSF3 (colony stimulating factor 3 receptor), CD40, TNFSF14 (tumor necrosis factor receptor superfamily, member 14), IFNB1 (interferon-β1), TIRAP (toll-interleukin 1 receptor domain containing adaptor protein), IL2RA (interleukin 2 receptor α), ETS1 (v-ets avian erythroblastosis virus E26 oncogene homolog 1), GADD45B (growth arrest and DNA-damage-inducible 45 β), BIRC3 (baculoviral IAP repeat containing 3), VAV1 (vav 1 guanine nucleotide exchange factor), COL5A1 (collagen, type V, α1), and C3 (complement component 3), have not been investigated thoroughly in the process of periodontitis. These genes are mainly involved in bacterial infection, immune response, and inflammatory reaction, suggesting that further characterizing their roles in periodontitis will be important. CONCLUSIONS A combination of computational tools will be useful in mining candidate genes for periodontitis. These theoretical results provide new clues for experimental biologists to plan targeted experiments.

A gene-based information gain method for detecting gene–gene interactions in case–control studies

Currently, most methods for detecting gene–gene interactions (GGIs) in genome-wide association studies are divided into SNP-based methods and gene-based methods. Generally, the gene-based methods can be more powerful than SNP-based methods. Some gene-based entropy methods can only capture the linear relationship between genes. We therefore proposed a nonparametric gene-based information gain method (GBIGM) that can capture both linear relationship and nonlinear correlation between genes. Through simulation with different odds ratio, sample size and prevalence rate, GBIGM was shown to be valid and more powerful than classic KCCU method and SNP-based entropy method. In the analysis of data from 17 genes on rheumatoid arthritis, GBIGM was more effective than the other two methods as it obtains fewer significant results, which was important for biological verification. Therefore, GBIGM is a suitable and powerful tool for detecting GGIs in case–control studies.

Assessing the patterns of linkage disequilibrium in genic regions of the human genome

We used the genotyping data generated by the International HapMap Project to study the patterns of linkage disequilibrium (LD) in human genic regions. LD patterns for 11 998 genes from 11 HapMap populations were identified by analyzing the distribution of haplotype blocks. The genes were prioritized using LD levels. The results showed that there were significant differences in the degree of LD between genes. Genes with high or low LD (the upper and lower quartiles of the LD levels) fell into different Gene Ontology functional categories. The high LD genes clustered preferentially in the metabolic process, macromolecule localization and cell‐cycle categories, whereas the low LD genes clustered in the developmental process, ion transport, and immune and regulation system categories. Furthermore, we subdivided the genic region into 3′‐UTR, 5′‐UTR and CDS (coding region), and compared the different LD patterns in these subregions. We found that the LD patterns in low LD genes had a more interspersed block structure compared with the high LD genes. This was especially true in the CDS and 5′‐UTR. The extent of LD was somewhat higher in 5′‐UTRs compared with 3′‐UTRs for both high and low LD genes. In addition, we assessed the overlap for the intragenic LD regions and found that the LD regions in high LD genes were more consistent among populations. Comprehensive information about the distribution of LD patterns in gene regions in populations may provide insights into the evolutionary history of humans and help in the selection of biomarkers for disease association studies.

Genome-wide haplotype association study identify TNFRSF1A, CASP7, LRP1B, CDH1 and TG genes associated with Alzheimer’s disease in Caribbean Hispanic individuals

Alzheimers disease (AD) is an acquired disorder of cognitive and behavioral impairment. It is considered to be caused by variety of factors, such as age, environment and genetic factors. In order to identify the genetic affect factors of AD, we carried out a bioinformatic approach which combined genome-wide haplotype-based association study with gene prioritization. The raw SNP genotypes data was downloaded from GEO database (GSE33528). It contains 615 AD patients and 560 controls of Caribbean Hispanic individuals. Firstly, we identified the linkage disequilibrium (LD) haplotype blocks and performed genome-wide haplotype association study to screen significant haplotypes that were associated with AD. Then we mapped these significant haplotypes to genes and obtained candidate genes set for AD. At last, we prioritized AD candidate genes based on their similarity with 36 known AD genes, so as to identify AD related genes. The results showed that 141 haplotypes on 134 LD blocks were significantly associated with AD (P<1E-4), and these significant haplotypes were mapped to 132 AD candidate genes. After prioritizing these candidate genes, we found seven AD related genes: APOE, APOC1, TNFRSF1A, LRP1B, CDH1, TG and CASP7. Among these genes, APOE and APOC1 are known AD risk genes. For the other five genes TNFRSF1A, CDH1, CASP7, LRP1B and TG, this is the first genetic association study which showed the significant association between these five genes and AD susceptibility in Caribbean Hispanic individuals. We believe that our findings can provide a new perspective to understand the genetic affect factors of AD.

The drug target genes show higher evolutionary conservation than non-target genes

Although evidence indicates that drug target genes share some common evolutionary features, there have been few studies analyzing evolutionary features of drug targets from an overall level. Therefore, we conducted an analysis which aimed to investigate the evolutionary characteristics of drug target genes. We compared the evolutionary conservation between human drug target genes and non-target genes by combining both the evolutionary features and network topological properties in human protein-protein interaction network. The evolution rate, conservation score and the percentage of orthologous genes of 21 species were included in our study. Meanwhile, four topological features including the average shortest path length, betweenness centrality, clustering coefficient and degree were considered for comparison analysis. Then we got four results as following: compared with non-drug target genes, 1) drug target genes had lower evolutionary rates; 2) drug target genes had higher conservation scores; 3) drug target genes had higher percentages of orthologous genes and 4) drug target genes had a tighter network structure including higher degrees, betweenness centrality, clustering coefficients and lower average shortest path lengths. These results demonstrate that drug target genes are more evolutionarily conserved than non-drug target genes. We hope that our study will provide valuable information for other researchers who are interested in evolutionary conservation of drug targets.

Disease embryo development network reveals the relationship between disease genes and embryo development genes

Abstract A basic problem for contemporary biology and medicine is exploring the correlation between human disease and underlying cellular mechanisms. For a long time, several efforts were made to reveal the similarity between embryo development and disease process, but few from the system level. In this article, we used the human protein–protein interactions (PPIs), disease genes with their classifications and embryo development genes and reconstructed a human disease-embryo development network to investigate the relationship between disease genes and embryo development genes. We found that disease genes and embryo development genes are prone to connect with each other. Furthermore, diseases can be categorized into three groups according to the closeness with embryo development in gene overlapping, interacting pattern in PPI network and co-regulated by microRNAs or transcription factors. Embryo development high-related disease genes show their closeness with embryo development at least in three biological levels. But it is not for embryo development medium-related disease genes and embryo development low-related disease genes. We also found that embryo development high-related disease genes are more central than other disease genes in the human PPI network. In addition, the results show that embryo development high-related disease genes tend to be essential genes compared with other diseases’ genes. This network-based approach could provide evidence for the intricate correlation between disease process and embryo development, and help to uncover potential mechanisms of human complex diseases.

Genome-wide pathway-based association analysis identifies risk pathways associated with Parkinson’s disease

Parkinsons disease (PD) is the second most common neurodegenerative disease. It is generally believed that it is influenced by both genetic and environmental factors, but the precise pathogenesis of PD is unknown to date. In this study, we performed a pathway analysis based on genome-wide association study (GWAS) to detect risk pathways of PD in three GWAS datasets. We first mapped all SNP markers to autosomal genes in each GWAS dataset. Then, we evaluated gene risk values using the minimum P-value of the tagSNPs. We took a pathway as a unit to identify the risk pathways based on the cumulative risks of the genes in the pathway. Finally, we combine the analysis results of the three datasets to detect the high risk pathways associated with PD. We found there were five same pathways in the three datasets. Besides, we also found there were five pathways which were shared in two datasets. Most of these pathways are associated with nervoussystem. Five pathways had been reported to be PD-related pathways in the previous literature. Our findings also implied that there was a close association between immune response and PD. Continued investigation of these pathways will further help us explain the pathogenesis of PD.

Genome-wide haplotype association analysis identifies SERPINB9, SERPINE2, GAK, and HSP90B1 as novel risk genes for oral squamous cell carcinoma

The oral squamous cell carcinoma (OSCC) is one of the most common malignant epithelial neoplasms and considered to be caused by the genetic damage. In addition, smoking habit and excessive alcohol consumption have been estimated to be the main risk factors. Although the association between OSCC and genetic susceptibility loci has been observed in many different populations, most of these studies simply focused on the single nucleotide polymorphism. Therefore, we made a contrast analysis between the 112 OSCC patients from the GEO database and 245 normal samples from the HapMap project. First, we performed a genome-wide haplotype association study by comparing the frequency of the haplotypes in the case–control experiment. Then, we mapped the haplotypes to the corresponding genes, screened the risk genes according to significant haplotypes (P < 1E−04), and prioritized the OSCC genes based on their similarity to the known OSCC susceptibility genes. We filtered four OSCC genes including SERPINB9, SERPINE2, GAK, and HSP90B1 through the gene global prioritization score (P < 0.005). SERPINB9 ranked first in the candidate gene list and contained a significant haplotype TAGGA (P value = 3.12E−11). The second risk gene was SERPINE2 with the haplotype GGGCCCTTT, which was closely similar to the SERPINB9.