GAB2 rs2373115 variant contributes to Alzheimer's disease risk specifically in European population

Association between the SLC6A11 rs2304725 and GABRG2 rs211037 polymorphisms and drug-resistant epilepsy: a meta-analysis

Background: Previous studies have shown that SLC6A11 and GABRG2 are linked to drug-resistant epilepsy (DRE), although there have been conflicting results in the literature. In this study, we systematically assessed the relationship between DRE and these two genes. Methods: We systematically searched the PubMed, Embase, Cochrane Library, Web of Science, Google Scholar, Wanfang Data, CNKI, and VIP databases. To clarify whether heterogeneity existed between studies, tools such as the Q-test and I ² statistic were selected. According to study heterogeneity, we chose fixed- or random-effects models for analysis. We then used the chi-squared ratio to evaluate any bias of the experimental data. Results: In total, 11 trials and 3,813 patients were selected. To investigate the relationship with DRE, we performed model tests on the two genes separately. The results showed that SLC6A11 rs2304725 had no significant correlation with DRE risk in the allele, dominant, recessive, and additive models in a pooled population. However, for the over-dominant model, DRE was correlated with rs2304725 (OR = 1.08, 95% CI: 0.92-1.27, p = 0.33) in a pooled population. Similarly, rs211037 was weakly significantly correlated with DRE for the dominant, recessive, over-dominant, and additive models in a pooled population. The subgroup analysis results showed that rs211037 expressed a genetic risk of DRE in allele (OR = 1.01, 95% CI: 0.76-1.35, p = 0.94), dominant (OR = 1.08, 95% CI: 0.77-1.50, p = 0.65), and additive models (OR = 1.14, 95% CI: 0.62-2.09, p = 0.67) in an Asian population. Conclusion: In this meta-analysis, our results showed that SLC6A11 rs2304725 and GABRG2 rs211037 are not significantly correlated with DRE. However, in the over-dominant model, rs2304725 was significantly correlated with DRE. Likewise, rs211037 conveyed a genetic risk for DRE in an Asian population in the allele, dominant, and additive models.

Variants rs2200733 and rs6843082 Show Different Associations in Asian and Non-Asian Populations With Ischemic Stroke

A previous genome-wide association study (GWAS) has reported that variants rs2200733 and rs6843082 in the paired-like homeodomain transcription factor 2 (PITX2) gene may be one of the risk factors for ischemic stroke (IS) in European populations. However, more recently, studies in Asia have reported that rs2200733 and rs6843082 are only weakly or not associated with increased risk of IS. This difference may be caused by the sample size and genetic heterogeneity of rs2200733 and rs6843082 among different races. For this study, we selected eight articles with nine studies from the PubMed and Embase databases, including five articles from Asian and three articles from non-Asian, to evaluate the risk of IS caused by rs2200733 and rs6843082. Then, we investigated rs2200733 and rs6843082 single-nucleotide polymorphisms (SNPs) by analysis using allele, recessive, dominant, and additive models. We identified that rs2200733 and rs6843082 are weakly significantly associated with IS for the allele model (p = 0.8), recessive model (p = 0.8), dominant model (p = 0.49), and additive model (p = 0.76) in a pooled population. Next, we performed a subgroup analysis of the population, the result of which showed that rs2200733 and rs6843082 covey genetic risk for IS in a non-Asian population, but not in an Asian population. In conclusion, our analysis shows that the effect of PITX2 rs2200733 and rs6843082 SNPs on IS risk in Asia is inconsistent with the effect observed in European IS cohorts.

The role and therapeutic implication of protein tyrosine phosphatases in Alzheimer's disease

Protein tyrosine phosphatases (PTPs) are important regulator of neuronal signal transduction and a growing number of PTPs have been implicated in Alzheimer's disease (AD). In the brains of patients with AD, there are a variety of abnormally phosphorylated proteins, which are closely related to the abnormal expression and activity of PTPs. β-Amyloid plaques (Aβ) and hyperphosphorylated tau protein are two pathological hallmarks of AD, and their accumulation ultimately leads to neurodegeneration. Studies have shown that protein phosphorylation signaling pathways mediates intracellular accumulation of Aβ and tau during AD development and are involved in synaptic plasticity and other stress responses. Here, we summarized the roles of PTPs related to the pathogenesis of AD and analyzed their therapeutic potential in AD.

A game changer for bipolar disorder diagnosis using RNA editing-based biomarkers

In clinical practice, differentiating Bipolar Disorder (BD) from unipolar depression is a challenge due to the depressive symptoms, which are the core presentations of both disorders. This misdiagnosis during depressive episodes results in a delay in proper treatment and a poor management of their condition. In a first step, using A-to-I RNA editome analysis, we discovered 646 variants (366 genes) differentially edited between depressed patients and healthy volunteers in a discovery cohort of 57 participants. After using stringent criteria and biological pathway analysis, candidate biomarkers from 8 genes were singled out and tested in a validation cohort of 410 participants. Combining the selected biomarkers with a machine learning approach achieved to discriminate depressed patients (n = 267) versus controls (n = 143) with an AUC of 0.930 (CI 95% [0.879-0.982]), a sensitivity of 84.0% and a specificity of 87.1%. In a second step by selecting among the depressed patients those with unipolar depression (n = 160) or BD (n = 95), we identified a combination of 6 biomarkers which allowed a differential diagnosis of bipolar disorder with an AUC of 0.935 and high specificity (Sp = 84.6%) and sensitivity (Se = 90.9%). The association of RNA editing variants modifications with depression subtypes and the use of artificial intelligence allowed developing a new tool to identify, among depressed patients, those suffering from BD. This test will help to reduce the misdiagnosis delay of bipolar patients, leading to an earlier implementation of a proper treatment.

Gab2 (Grb2-Associated Binder2) Plays a Crucial Role in Inflammatory Signaling and Endothelial Dysfunction

[Figure: see text].

Network-based analysis on genetic variants reveals the immunological mechanism underlying Alzheimer's disease

Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by the impairment of cognitive function and loss of memory. Previous studies indicate an essential role of immune response in AD, but the detailed mechanisms remain unclear. In this study, we obtained 1664 credible risk variants (CRVs) based on the most significant SNP detected by International Genomics of Alzheimer's Project, from which 99 genes (CRVs-related genes) were identified. Function analysis revealed that these genes were mainly involved in immune response and amyloid-β and its precursor metabolisms, indicating a potential role of immune response in regulating neurobiological processes in the etiology of neurodegenerative disease. Pathway crosstalk analysis revealed the complicated connections between immune-related pathways. Further, we found that the CRVs-related genes showed temporal-specific expression in the thalamus in adolescence developmental period. Cell type-specific expression analysis found that CRVs-related genes might be specifically expressed in brain cells such as astrocytes and oligodendrocytes. Protein-protein interaction network analysis identified the highly interconnected 'hub' genes, all of which were susceptible loci of AD. These results indicated that the CRVs may exert a potential influence in AD by regulating immune response, thalamus development, astrocytes activities, and amyloid-β binding. Our results provided hints for further experimental verification of AD pathophysiology.

System-Level Analysis of Alzheimer's Disease Prioritizes Candidate Genes for Neurodegeneration

Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder. Since the advent of the genome-wide association study (GWAS) we have come to understand much about the genes involved in AD heritability and pathophysiology. Large case-control meta-GWAS studies have increased our ability to prioritize weaker effect alleles, while the recent development of network-based functional prediction has provided a mechanism by which we can use machine learning to reprioritize GWAS hits in the functional context of relevant brain tissues like the hippocampus and amygdala. In parallel with these developments, groups like the Alzheimer’s Disease Neuroimaging Initiative (ADNI) have compiled rich compendia of AD patient data including genotype and biomarker information, including derived volume measures for relevant structures like the hippocampus and the amygdala. In this study we wanted to identify genes involved in AD-related atrophy of these two structures, which are often critically impaired over the course of the disease. To do this we developed a combined score prioritization method which uses the cumulative distribution function of a gene’s functional and positional score, to prioritize top genes that not only segregate with disease status, but also with hippocampal and amygdalar atrophy. Our method identified a mix of genes that had previously been identified in AD GWAS including APOE, TOMM40, and NECTIN2(PVRL2) and several others that have not been identified in AD genetic studies, but play integral roles in AD-effected functional pathways including IQSEC1, PFN1, and PAK2. Our findings support the viability of our novel combined score as a method for prioritizing region- and even cell-specific AD risk genes.

Impact of serum calcium levels on total body bone mineral density: A mendelian randomization study in five age strata

BACKGROUND & AIMS

Mendelian randomization (MR) studies have reported the causal association between serum calcium levels and bone mineral density (BMD). The results showed that genetically increased serum calcium levels in individuals with normal calcium levels did not increase BMD and could even reduce BMD. However, whether there are differences in the association between serum calcium and BMD in different age strata remains unclear.

METHODS

We selected eight serum calcium genetic variants with genome-wide significance (P < 5.00E-08) as the potential instrumental variables. We conducted an MR analysis to evaluate the impact of serum calcium levels on total body BMD in five age strata, 0-15, 15-30, 30-45, 45-60, and ≥60 years, using large-scale serum calcium (61,079 individuals) and total body BMD genome-wide association study (66,628 individuals) datasets. For pleiotropy analysis, we used a manual method and four common statistical methods, namely the MR-Egger intercept, MR-PRESSO, heterogeneity, and Steiger filtering tests. For MR analysis, we selected four MR methods, namely inverse-variance weighted, weighted median, MR-Egger, and MR-PRESSO. In addition to the univariable MR analysis, we conducted a multivariate MR analysis taking into account the effect of serum parathyroid hormone levels.

RESULTS

Univariable MR analysis using the inverse-variance weighted method indicated that per 0.5-mg/dL increase (about 1 standard deviation) in serum calcium levels was statistically significantly associated with reduced total body BMD only in the ≥60 years stratum (effect estimate (beta) = -0.545, 95% confidence interval (CI): -0.892 to -0.198, P = 0.002). The weighted median regression (beta = -0.446, 95% CI: -0.821 to -0.094, P = 1.40E-02) and MR-PRESSO (beta = -0.545, 95% CI: -0.892 to -0.198, P = 0.022) MR methods further supported this suggestive association. The multivariable MR analysis also found a significant association between increased serum calcium levels and reduced total body BMD in the ≥60 years stratum (beta = -0.547, 95% CI: -0.934 to -0.16, P = 0.006).

CONCLUSIONS

Our results provide genetic evidence that increased serum calcium levels did not improve BMD in the general population and that the elevated serum calcium levels in generally healthy populations, especially in adults older than 60 years, may even reduce the BMD. Our results are comparable with those of recent MR findings.

PLCG2 rs72824905 Variant Reduces the Risk of Alzheimer's Disease and Multiple Sclerosis

We aimed to evaluate the association of PLCG2 rs72824905 variant with Alzheimer's disease (AD) and multiple sclerosis (MS) using large-scale genetic association study datasets. We selected 50,024 AD cases and 467,330 controls, and 32,367 MS cases and 36,012 controls. We found moderate heterogeneity of rs72824905 in different studies. We found significant association between rs72824905 G allele and reduced AD risk (OR = 0.66, 95% CI 0.59-0.74, p = 5.91E-14). Importantly, rs72824905 G allele could also significantly reduce the risk of MS with OR = 0.94, p = 3.63E-05. Hence, the effects of rs72824905 on AD and MS are consistent.

rs1990622 variant associates with Alzheimer's disease and regulates TMEM106B expression in human brain tissues

BACKGROUND

It has been well established that the TMEM106B gene rs1990622 variant was a frontotemporal dementia (FTD) risk factor. Until recently, growing evidence highlights the role of TMEM106B in Alzheimer's disease (AD). However, it remains largely unclear about the role of rs1990622 variant in AD.

METHODS

Here, we conducted comprehensive analyses including genetic association study, gene expression analysis, eQTLs analysis, and colocalization analysis. In stage 1, we conducted a genetic association analysis of rs1990622 using large-scale genome-wide association study (GWAS) datasets from International Genomics of Alzheimer's Project (21,982 AD and 41,944 cognitively normal controls) and UK Biobank (314,278 participants). In stage 2, we performed a gene expression analysis of TMEM106B in 49 different human tissues using the gene expression data in GTEx. In stage 3, we performed an expression quantitative trait loci (eQTLs) analysis using multiple datasets from UKBEC, GTEx, and Mayo RNAseq Study. In stage 4, we performed a colocalization analysis to provide evidence of the AD GWAS and eQTLs pair influencing both AD and the TMEM106B expression at a particular region.

RESULTS

We found (1) rs1990622 variant T allele contributed to AD risk. A sex-specific analysis in UK Biobank further indicated that rs1990622 T allele only contributed to increased AD risk in females, but not in males; (2) TMEM106B showed different expression in different human brain tissues especially high expression in cerebellum; (3) rs1990622 variant could regulate the expression of TMEM106B in human brain tissues, which vary considerably in different disease statuses, the mean ages at death, the percents of females, and the different descents of the selected donors; (4) colocalization analysis provided suggestive evidence that the same variant contributed to AD risk and TMEM106B expression in cerebellum.

CONCLUSION

Our comprehensive analyses highlighted the role of FTD rs1990622 variant in AD risk. This cross-disease approach may delineate disease-specific and common features, which will be important for both diagnostic and therapeutic development purposes. Meanwhile, these findings highlight the importance to better understand TMEM106B function and dysfunction in the context of normal aging and neurodegenerative diseases.

BIN1 rs744373 variant shows different association with Alzheimer's disease in Caucasian and Asian populations

BACKGROUND

The association between BIN1 rs744373 variant and Alzheimer's disease (AD) had been identified by genome-wide association studies (GWASs) as well as candidate gene studies in Caucasian populations. But in East Asian populations, both positive and negative results had been identified by association studies. Considering the smaller sample sizes of the studies in East Asian, we believe that the results did not have enough statistical power.

RESULTS

We conducted a meta-analysis with 71,168 samples (22,395 AD cases and 48,773 controls, from 37 studies of 19 articles). Based on the additive model, we observed significant genetic heterogeneities in pooled populations as well as Caucasians and East Asians. We identified a significant association between rs744373 polymorphism with AD in pooled populations (P = 5 × 10- 07, odds ratio (OR) = 1.12, and 95% confidence interval (CI) 1.07-1.17) and in Caucasian populations (P = 3.38 × 10- 08, OR = 1.16, 95% CI 1.10-1.22). But in the East Asian populations, the association was not identified (P = 0.393, OR = 1.057, and 95% CI 0.95-1.15). Besides, the regression analysis suggested no significant publication bias. The results for sensitivity analysis as well as meta-analysis under the dominant model and recessive model remained consistent, which demonstrated the reliability of our finding.

CONCLUSIONS

The large-scale meta-analysis highlighted the significant association between rs744373 polymorphism and AD risk in Caucasian populations but not in the East Asian populations.

A Review of Recent Advances and Research on Drug Target Identification Methods

BACKGROUND

From a therapeutic viewpoint, understanding how drugs bind and regulate the functions of their target proteins to protect against disease is crucial. The identification of drug targets plays a significant role in drug discovery and studying the mechanisms of diseases. Therefore the development of methods to identify drug targets has become a popular issue.

METHODS

We systematically review the recent work on identifying drug targets from the view of data and method. We compiled several databases that collect data more comprehensively and introduced several commonly used databases. Then divided the methods into two categories: biological experiments and machine learning, each of which is subdivided into different subclasses and described in detail.

RESULTS

Machine learning algorithms are the majority of new methods. Generally, an optimal set of features is chosen to predict successful new drug targets with similar properties. The most widely used features include sequence properties, network topological features, structural properties, and subcellular locations. Since various machine learning methods exist, improving their performance requires combining a better subset of features and choosing the appropriate model for the various datasets involved.

CONCLUSION

The application of experimental and computational methods in protein drug target identification has become increasingly popular in recent years. Current biological and computational methods still have many limitations due to unbalanced and incomplete datasets or imperfect feature selection methods.

ProbPFP: a multiple sequence alignment algorithm combining hidden Markov model optimized by particle swarm optimization with partition function

BACKGROUND

During procedures for conducting multiple sequence alignment, that is so essential to use the substitution score of pairwise alignment. To compute adaptive scores for alignment, researchers usually use Hidden Markov Model or probabilistic consistency methods such as partition function. Recent studies show that optimizing the parameters for hidden Markov model, as well as integrating hidden Markov model with partition function can raise the accuracy of alignment. The combination of partition function and optimized HMM, which could further improve the alignment's accuracy, however, was ignored by these researches.

RESULTS

A novel algorithm for MSA called ProbPFP is presented in this paper. It intergrate optimized HMM by particle swarm with partition function. The algorithm of PSO was applied to optimize HMM's parameters. After that, the posterior probability obtained by the HMM was combined with the one obtained by partition function, and thus to calculate an integrated substitution score for alignment. In order to evaluate the effectiveness of ProbPFP, we compared it with 13 outstanding or classic MSA methods. The results demonstrate that the alignments obtained by ProbPFP got the maximum mean TC scores and mean SP scores on these two benchmark datasets: SABmark and OXBench, and it got the second highest mean TC scores and mean SP scores on the benchmark dataset BAliBASE. ProbPFP is also compared with 4 other outstanding methods, by reconstructing the phylogenetic trees for six protein families extracted from the database TreeFam, based on the alignments obtained by these 5 methods. The result indicates that the reference trees are closer to the phylogenetic trees reconstructed from the alignments obtained by ProbPFP than the other methods.

CONCLUSIONS

We propose a new multiple sequence alignment method combining optimized HMM and partition function in this paper. The performance validates this method could make a great improvement of the alignment's accuracy.

Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach

Background

Machine learning can effectively nominate novel genes for various research purposes in the laboratory. On a genome-wide scale, we implemented multiple databases and algorithms to predict and prioritize the human aging genes (PPHAGE).

Results

We fused data from 11 databases, and used Naïve Bayes classifier and positive unlabeled learning (PUL) methods, NB, Spy, and Rocchio-SVM, to rank human genes in respect with their implication in aging. The PUL methods enabled us to identify a list of negative (non-aging) genes to use alongside the seed (known age-related) genes in the ranking process. Comparison of the PUL algorithms revealed that none of the methods for identifying a negative sample were advantageous over other methods, and their simultaneous use in a form of fusion was critical for obtaining optimal results (PPHAGE is publicly available at https://cbb.ut.ac.ir/pphage).

Conclusion

We predict and prioritize over 3,000 candidate age-related genes in human, based on significant ranking scores. The identified candidate genes are associated with pathways, ontologies, and diseases that are linked to aging, such as cancer and diabetes. Our data offer a platform for future experimental research on the genetic and biological aspects of aging. Additionally, we demonstrate that fusion of PUL methods and data sources can be successfully used for aging and disease candidate gene prioritization.

EZH2 RIP-seq Identifies Tissue-specific Long Non-coding RNAs

Background:

Polycomb Repressive Complex 2 (PRC2) catalyzes histone methylation at H3 Lys27, and plays crucial roles during development and diseases in numerous systems. Its catalytic sub-unit EZH2 represents a key nuclear target for long non-coding RNAs (lncRNAs) that emerging to be a novel class of epigenetic regulator and participate in diverse cellular processes. LncRNAs are character-ized by high tissue-specificity; however, little is known about the tissue profile of the EZH2-interacting lncRNAs.

Objective:

Here we performed a global screening for EZH2-binding lncRNAs in tissues including brain, lung, heart, liver, kidney, intestine, spleen, testis, muscle and blood by combining RNA immuno-precipitation and RNA sequencing. We identified 1328 EZH2-binding lncRNAs, among which 470 were shared in at least two tissues while 858 were only detected in single tissue. An RNA motif with specific secondary structure was identified in a number of lncRNAs, albeit not in all EZH2-binding lncRNAs. The EZH2-binding lncRNAs fell into four categories including intergenic lncRNA, antisense lncRNA, intron-related lncRNA and promoter-related lncRNA, suggesting diverse regulations of both cis and trans-mechanisms. A promoter-related lncRNA Hnf1aos1 bound to EZH2 specifically in the liver, a feature same as its paired coding gene Hnf1a, further confirming the validity of our study. In ad-dition to the well known EZH2-binding lncRNAs like Kcnq1ot1, Gas5, Meg3, Hotair and Malat1, ma-jority of the lncRNAs were firstly reported to be associated with EZH2.

Conclusion:

Our findings provide a profiling view of the EZH2-interacting lncRNAs across different tissues, and suggest critical roles of lncRNAs during cell differentiation and maturation

Tau interacts with SHP2 in neuronal systems and in Alzheimer's disease brains

Microtubule-associated protein tau, an integral component of neurofibrillary tangles, interacts with a variety of signaling molecules. Previously, our laboratory reported that nerve growth factor (NGF)-induced MAPK activation in a PC12-derived cell line was potentiated by tau, with phosphorylation at T231 being required. Therefore, we sought to identify a signaling molecule involved in the NGF-induced Ras-MAPK pathway that interacted with phospho-T231-tau. Here, we report that the protein tyrosine phosphatase SHP2 (also known as PTPN11) interacted with tau, with phospho-T231 significantly enhancing the interaction. By using proximity ligation assays, we found that endogenous tau-SHP2 complexes were present in neuronal cells, where the number of tau-SHP2 complexes significantly increased when the cells were treated with NGF, with phosphorylation at T231 being required for the increase. The interaction did not require microtubule association, and an association between tau and activated SHP2 was also found. Tau-SHP2 complexes were also found in both primary mouse hippocampal cultures and adult mouse brain. Finally, SHP2 levels were upregulated in samples from patients with mild and severe Alzheimer's disease (AD), and the level of tau-SHP2 complexes were increased in AD patient samples. These findings strongly suggest a role for the tau-SHP2 interaction in NGF-stimulated neuronal development and in AD.This article has an associated First Person interview with the first author of the paper.

Polygenic Risk Score for Alzheimer's Disease Is Associated With Ch4 Volume in Normal Subjects

Alzheimer's disease (AD) is a common neurodegenerative disease. APOE is the strong genetic risk factor of AD. The existing genome-wide association studies have identified many single nucleotide polymorphisms (SNPs) with minor effects on AD risk and the polygenic risk score (PRS) is presented to combine the effect of these SNPs. On the other hand, the volumes of various brain regions in AD patients have significant changes compared to that in normal individuals. Ch4 brain region containing at least 90% cholinergic neurons is the most extensive and conspicuous in the basal forebrain. Here, we investigated the relationship between the combined effect of AD-associated SNPs and Ch4 volume using the PRS approach. Our results showed that Ch4 volume in AD patients is significantly different from that in normal control subjects (p-value < 2.2 × 10-16). AD PRS, is not associated with the Ch4 volume in AD patients, excluding the APOE region (p-value = 0.264) and including the APOE region (p-value = 0.213). However, AD best-fit PRS, excluding the APOE region, is associated with Ch4 volume in normal control subjects (p-value = 0.015). AD PRS based on 8070 SNPs could explain 3.35% variance of Ch4 volume. In addition, the p-value of AD PRS model in normal control subjects, including the APOE region, is 0.006. AD PRS based on 8079 SNPs could explain 4.23% variance of Ch4 volume. In conclusion, PRS based on AD-associated SNPs is significantly related to Ch4 volume in normal subjects but not in patients.

A Novel Protein Subcellular Localization Method With CNN-XGBoost Model for Alzheimer's Disease

The disorder distribution of protein in the compartment or organelle leads to many human diseases, including neurodegenerative diseases such as Alzheimer's disease. The prediction of protein subcellular localization play important roles in the understanding of the mechanism of protein function, pathogenes and disease therapy. This paper proposes a novel subcellular localization method by integrating the Convolutional Neural Network (CNN) and eXtreme Gradient Boosting (XGBoost), where CNN acts as a feature extractor to automatically obtain features from the original sequence information and a XGBoost classifier as a recognizer to identify the protein subcellular localization based on the output of the CNN. Experiments are implemented on three protein datasets. The results prove that the CNN-XGBoost method performs better than the general protein subcellular localization methods.

Alzheimer's Disease rs11767557 Variant Regulates EPHA1 Gene Expression Specifically in Human Whole Blood

Large-scale genome-wide association studies have reported EPHA1 rs11767557 variant to be associated with Alzheimer's disease (AD) risk in the European population. However, it is still unclear how this variant functionally contributes to the underlying disease pathogenesis. The rs11767557 variant is located approximately 3 kb upstream of EPHA1 gene. We think that rs11767557 may modify the expression of nearby genes such as EPHA1 and further cause AD risk. Until now, the potential association between rs11767557 and the expression of nearby genes has not been reported in previous studies. Here, we evaluate the potential expression association between rs11767557 and EPHA1 using multiple large-scale eQTLs datasets in human brain tissues and the whole blood. The results show that rs11767557 variant could significantly regulate EPHA1 gene expression specifically in human whole blood. These findings may further provide important supplementary information about the regulating mechanisms of rs11767557 variant in AD risk.

Relationship Between Alzheimer's Disease and the Immune System: A Meta-Analysis of Differentially Expressed Genes

Alzheimer's disease (AD), a neurodegenerative diseases (neuro-diseases) which is prevalent in the elderly and seriously affects the lives of individuals. Many studies have discussed the relationship between immune system and AD pathogenesis. Here, the meta-analysis of differentially expressed (DE) genes based on microarray data was conducted to study the association between AD and immune system. 9519 target genes of hippocampus in 146 subjects (73 AD cases and 73 controls) from 4 microarray data sets were compiled and DE genes with p < 1.00E - 04 were selected to conduct the pathway-analysis. The results indicated that the DE genes were significantly enriched in the neuro-diseases as well as the immune system pathways.

Prediction of Alzheimer's Disease-Associated Genes by Integration of GWAS Summary Data and Expression Data

Alzheimer's disease (AD) is the most common cause of dementia. It is the fifth leading cause of death among elderly people. With high genetic heritability (79%), finding the disease's causal genes is a crucial step in finding a treatment for AD. Following the International Genomics of Alzheimer's Project (IGAP), many disease-associated genes have been identified; however, we do not have enough knowledge about how those disease-associated genes affect gene expression and disease-related pathways. We integrated GWAS summary data from IGAP and five different expression-level data by using the transcriptome-wide association study method and identified 15 disease-causal genes under strict multiple testing (α < 0.05), and four genes are newly identified. We identified an additional 29 potential disease-causal genes under a false discovery rate (α < 0.05), and 21 of them are newly identified. Many genes we identified are also associated with an autoimmune disorder.

Influence of ADAM10 Polymorphisms on Plasma Level of Soluble Receptor for Advanced Glycation End Products and The Association With Alzheimer's Disease Risk

To determine the role of A disintegrin and metalloproteinase 10 (ADAM10) in genetic susceptibility to Alzheimer's disease (AD) in a representative Chinese sample, we genotyped 362 AD patients and 370 healthy controls for the rs514049A/C and rs653765C/T polymorphisms in the ADAM10 promoter using the SNaPshot technique. We also examined the potential impact of these polymorphisms on the plasma level of soluble receptor for advanced glycation end products (sRAGE), a decoy receptor whose reduction has been associated with a higher risk of AD. Additionally, a meta-analysis was performed using the present study and the largest GWAS from the International Genomics of Alzheimer's Project (IGAP). No significant differences were found in the distributions of genotypes or alleles between AD patients and control subjects. However, age-at-onset stratification analysis revealed that there were significant differences in the genotypes (P = 0.015) and alleles (P = 0.006) of the rs653765 SNP. Furthermore, patients with the rs653765 CC genotype showed a lower ADAM10 level and a faster cognitive deterioration than those in patients with the CT/TT genotype in late-onset AD (LOAD), and the rs653765 CC polymorphism was able to regulate the production of the ADAM10 substrate sRAGE. In contrast, the rs514049 polymorphism was not statistically associated with AD. In the meta-analysis, we observed that both rs514049 (A allele vs. C allele, P = 0.002) and rs653765 (C allele vs. T allele, P = 0.004) were associated with AD risk. The present study indicated that the rs653765 polymorphism might be associated with the risk and development of LOAD; in particular, the risk genotype, CC, may decrease the expression of ADAM10, influencing the plasma levels of sRAGE, and thus may be correlated with the clinical progression of AD.

Alzheimer's Disease Risk Variant rs2373115 Regulates GAB2 and NARS2 Expression in Human Brain Tissues

Genetic association studies have identified significant association between the GAB2 rs2373115 variant and Alzheimer's disease (AD). However, it is unknown whether rs2373115 affects the regulation of nearby genes. Here, we evaluate the potential effect of rs2373115 on gene expression using multiple eQTL (expression quantitative trait loci) datasets from human brain tissues from the Mayo Clinic brain expression genome-wide association study (eGWAS), the UK Brain Expression Consortium (UKBEC), the Genotype-Tissue Expression (GTEx) project, and the Brain xQTL Serve. Our findings indicate that the rs2373115 C allele is associated with increased NARS2 expression, and both reduced and increased GAB2 expression in human tissues. Using a large-scale AD case-control expression dataset, we found increased GAB2 expression and reduced NARS2 expression in AD cases compared with controls. We believe that our findings provide important information regarding the rs2373115 variant and expression of nearby genes with respect to AD risk.

GLRB variants regulate nearby gene expression in human brain tissues

A recent genome-wide association study (GWAS) identified four genetic variants rs78726293, rs191260602, rs17035816 and rs7688285 in GLRB gene to be associated with panic disorder (PD) risk. In fact, GWAS is an important first step to investigate the genetics of human complex diseases. In order to translate into opportunities for new diagnostics and therapies, we must identify the genes perturbed by these four variants, and understand how these variant functionally contributes to the underlying disease pathogenesis. Here, we investigated the effect of these four genetic variants and the expression of three nearby genes including PDGFC, GLRB and GRIA2 in human brain tissues using the GTEx (version 6) and Braineac eQTLs datasets. In GTEx (version 6) dataset, the results showed that both rs17035816 and rs7688285 variants could significantly regulate PDGFC and GLRB gene expression. In Braineac dataset, the results showed that rs17035816 variant could significantly regulate GLRB and GRIA2 gene expression. We believe that these findings further provide important supplementary information about the regulating mechanisms of rs17035816 and rs7688285 variants in PD risk.

Common genetic variant rs3802842 in 11q23 contributes to colorectal cancer risk in Chinese population

A genome-wide association study identified a common genetic variant rs3802842 at 11q23 to be associated with CRC risk with OR=1.1 and P = 5.80E-10 in European population. In Chinese population, several genetic association studies have investigated the association between rs3802842 variant and CRC risk. However these studies reported both positive and negative association results. It is still necessary to evaluate a specific variant in a specific population, which would be informative to reveal the disease mechanism. Until recently, there is no a systemic study to evaluate the potential association between rs3802842 and CRC risk in Chinese population by a meta-analysis method. Here, we aim to evaluate this association in Chinese population by a meta-analysis method using 12077 samples including 5816 CRC cases and 6261 controls. We identified the T allele of rs3802842 to be significantly related with an increase CRC risk (P=2.22E-05, OR=1.14, 95% CI 1.07-1.21) in Chinese population.