Yongshuai Jiang

Cardiovascular disease contributes to Alzheimer's disease: evidence from large-scale genome-wide association studies

Alzheimers disease (AD) is the most common and complex neurodegenerative disease in the elderly individuals. Recently, genome-wide association studies (GWAS) have been used to investigate AD pathogenesis. These GWAS have yielded important new insights into the genetic mechanisms of AD. However, these newly identified AD susceptibility loci exert only very small risk effects and cannot fully explain the underlying AD genetic risk. We hypothesize that combining the findings from different AD GWAS may have greater power than genetic analysis alone. To identify new AD risk factors, we integrated findings from 3 previous large-scale AD GWAS (n = 14,138) using a gene-based meta-analysis and subsequently conducted a pathway analysis using the kyoto encyclopedia of genes and genomes and gene ontology databases. Interestingly, we not only confirmed previous findings, but also highlighted, for the first time, the involvement of cardiovascular disease-related pathways in AD. Our results provided the clues as to the link between these diseases using pathway analysis methods. We believe that these findings will be very useful for future genetic studies of AD.

PICALM Gene rs3851179 Polymorphism Contributes to Alzheimer’s Disease in an Asian Population

PICALM gene rs3851179 polymorphism was reported to an Alzheimer’s disease (AD) susceptibility locus in a Caucasian population. However, recent studies reported consistent and inconsistent results in an Asian population. Four studies indicated no association between rs3851179 and AD in a Chinese population and one study reported weak association in a Japanese population. We consider that the failure to replicate the significant association between rs3851179 and AD may be caused by at least two reasons. The first reason may be the genetic heterogeneity in AD among different populations, and the second may be the relatively small sample size compared with large-scale GWAS in Caucasian ancestry. In order to confirm this view, in this research, we first evaluated the genetic heterogeneity of rs3851179 polymorphism in Caucasian and Asian populations. We then investigated rs3851179 polymorphism in an Asian population by a pooled analysis method and a meta-analysis method. We did not observe significant genetic heterogeneity of rs3851179 in the Caucasian and Asian populations. Our results indicate that rs3851179 polymorphism is significantly associated with AD in the Asian population by both pooled analysis and meta-analysis methods. We believe that our findings will be very useful for future genetic studies in AD.

Association between the -1306C/T polymorphism of matrix metalloproteinase-2 gene and lumbar disc disease in Chinese young adults

Matrix metalloproteinase-2 (MMP-2) has been shown to play a pivotal role in the pathophysiology of lumbar disc disease (LDD). Increased expression and activity of MMP-2 has been documented in degenerative discs. The polymorphism -1306C/T in the promoter region of MMP-2 gene was reported to influence gene transcription and expression. The objective of this study was therefore to investigate the possible association of MMP-2 -1306C/T polymorphism with the occurrence and the clinical characteristics of LDD. MMP-2 genotypes were determined by polymerase chain reaction (PCR) and direct DNA sequencing in a case-control study involving 162 younger patients with LDD and 318 age- and sex-matched healthy adults. The results showed that the frequency of MMP-2 -1306CC genotype was significantly higher in LDD patients when compared with controls. Subjects with the CC genotype had nearly threefold increased risk for LDD (odds ratio 3.08; 95% confidence interval 1.84–5.16) compared with subjects carrying at least one variant T allele. Furthermore, this genotype was found to correlate with more severe grades of disc degeneration observed on magnetic resonance imaging scan. These findings suggest that MMP-2 -1306C/T polymorphism may be a genetic risk factor related to LDD susceptibility in the young adult population.

BIN1 gene rs744373 polymorphism contributes to Alzheimer's disease in East Asian population

Large-scale genome-wide association studies (GWAS) identified BIN1 gene rs744373 polymorphism to be significantly associated with Alzheimers disease (AD) in Caucasian ancestry. Recently, this polymorphism was also investigated in East Asian population. However, no study reported significant association. We consider that the failure to replicate significant association between rs744373 polymorphism and AD may be caused by the relatively small sample size. In this research, we evaluated this association using pooled samples from previous studies (n=4982, 1437 AD cases and 3545 controls). Two methods including pooled analysis and meta-analysis were used to investigate the association. Using the pooled analysis, we observed significant association between rs744373 polymorphism and AD by both genotype test (P=3.94E-03, 4.59E-03 and 1.04E-02) and allele test (P=1.12E-03, OR=1.16, 95% CI 1.06-1.28). Interestingly, the meta-analysis confirmed this association with P=8.00E-03 (OR=1.14, 95% CI 1.03-1.25) and P=2.00E-02 (OR=1.16, 95% CI 1.02-1.32). We also evaluated the effect of rs744373 polymorphism on AD risk in different ethnic backgrounds and found that rs744373 polymorphism contributed to AD with similar genetic risk in East Asian and Caucasian populations. To our knowledge, this is the first study to show significant association between rs744373 polymorphism and AD in East Asian population.

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.

Alzheimer's Disease Variants with the Genome-Wide Significance are Significantly Enriched in Immune Pathways and Active in Immune Cells.

The existing large-scale genome-wide association studies (GWAS) datasets provide strong support for investigating the mechanisms of Alzheimer’s disease (AD) by applying multiple methods of pathway analysis. Previous studies using selected single nucleotide polymorphisms (SNPs) with several thresholds of nominal significance for pathway analysis determined that the threshold chosen for SNPs can reflect the disease model. Presumably, then, pathway analysis with a stringent threshold to define “associated” SNPs would test the hypothesis that highly associated SNPs are enriched in one or more particular pathways. Here, we selected 599 AD variants (P < 5.00E−08) to investigate the pathways in which these variants are enriched and the cell types in which these variants are active. Our results showed that AD variants are significantly enriched in pathways of the immune system. Further analysis indicated that AD variants are significantly enriched for enhancers in a number of cell types, in particular the B-lymphocyte, which is the most substantially enriched cell type. This cell type maintains its dominance among the strongest enhancers. AD SNPs also display significant enrichment for DNase in 12 cell types, among which the top 6 significant signals are from immune cell types, including 4 B cells (top 4 significant signals) and CD14+ and CD34+ cells. In summary, our results show that these AD variants with P < 5.00E−08 are significantly enriched in pathways of the immune system and active in immune cells. To a certain degree, the genetic predisposition for development of AD is rooted in the immune system, rather than in neuronal cells.

Cell adhesion molecule pathway genes are regulated by cis-regulatory SNPs and show significantly altered expression in Alzheimer's disease brains.

We previously identified the cell adhesion molecule (CAM) pathway as a consistent signal in 2 Alzheimers disease (AD) genome-wide association studies (GWAS). However, the genetic mechanisms of the CAM pathway in AD are unclear. Here, we conducted pathway analysis using (1) Kyoto Encyclopedia of Genes and Genomes and Gene Ontology pathways; (2) 4 brain expression GWAS datasets; and (3) 2 whole-genome AD case-control expression datasets. Using the 4 brain expression GWAS datasets, we identified that genes regulated by cis-regulatory single-nucleotide polymorphisms (SNPs) were significantly enriched in the CAM pathway (p = 2.05E-06, p = 6.10E-07, p = 2.05E-06, and p = 1.47E-07 for each dataset). Interestingly, CAM is a significantly enriched pathway using down-regulated genes (raw p = 0.0235 and adjusted p = 0.0305) and all differentially expressed genes (raw p = 0.0105 and adjusted p = 0.0156) in dataset 5, and all differentially expressed genes (raw p = 0.0041 and adjusted p = 0.0062) in dataset 6. Collectively, our results show that CAM pathway genes are regulated by cis-regulatory SNPs and show significantly altered expression in AD. We believe that our results advance the understanding of AD mechanisms and will be useful for future genetic studies of AD.

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.

Lack of association between PICALM rs3851179 polymorphism and Alzheimer's disease in Chinese population and APOEε4-negative subgroup

Recently, the association between PICALM rs3851179 polymorphism and Alzheimers disease (AD) was investigated in the Chinese population by 3 independent studies. However, both allele and genotype tests failed to reveal any association. The association was identified only in the APOEε4-negative subgroup. We think that the failure to replicate the association may be because of the relatively small sample size. In this research, we reinvestigated the association using all the samples from these 3 studies (n = 2486, and 1202 cases and 1284 control subjects). We failed to replicate this association between the rs3851179 polymorphism and AD in all samples and the APOEε4-negative subgroup. Our results indicate that rs3851179 may not be an AD susceptibility locus in the Chinese population and the APOEε4-negative subgroup.

Predict potential drug targets from the ion channel proteins based on SVM.

The identification of molecular targets is a critical step in the drug discovery and development process. Ion channel proteins represent highly attractive drug targets implicated in a diverse range of disorders, in particular in the cardiovascular and central nervous systems. Due to the limits of experimental technique and low-throughput nature of patch-clamp electrophysiology, they remain a target class waiting to be exploited. In our study, we combined three types of protein features, primary sequence, secondary structure and subcellular localization to predict potential drug targets from ion channel proteins applying classical support vector machine (SVM) method. In addition, our prediction comprised two stages. In stage 1, we predicted ion channel target proteins based on whole-genome target protein characteristics. Firstly, we performed feature selection by Mann-Whitney U test, then made predictions to identify potential ion channel targets by SVM and designed a new evaluating indicator Q to prioritize results. In stage 2, we made a prediction based on known ion channel target protein characteristics. Genetic algorithm was used to select features and SVM was used to predict ion channel targets. Then, we integrated results of two stages, and found that five ion channel proteins appeared in both prediction results including CGMP-gated cation channel beta subunit and Gamma-aminobutyric acid receptor subunit alpha-5, etc., and four of which were relative to some nerve diseases. It suggests that these five proteins are potential targets for drug discovery and our prediction strategies are effective.