Functional annotation of Alzheimer's disease associated loci revealed by GWASs

Analysis of Alzheimer's disease associated deleterious non-synonymous single nucleotide polymorphisms and their impacts on protein structure and function by performing in-silico methods

Alzheimer's disease (AD) is a neurodegenerative disorder that is presented with a progressive loss of memory, a decline in cognitive abilities and multiple changes in behavior. Its pathogenicity has been linked to genetic factors in approximately 60-80% of the cases specifically APOE gene family and as well as other gene families. This study utilized advanced computational biology methods to analyze AD-associated nsSNPs extracted from the NHGRI-EBI GWAS Catalog. Ensembl Variant Effect Predictor (VEP) is used to annotate the variants associated with AD. Annotated missense variants were subjected to PolyPhen-2, SNPs&Go, PredictSNP servers which were used to predict pathogenicity of selected missense variants by protein sequence information. DynaMut and DUET servers were applied to determine protein stability due to the amino acid change by integrating protein structure information. Missense variations associated with AD were annotated to 26 proteins and further analyzed in our study. Following rigorous data filtration steps, 15 candidate variants (13 proteins) were identified and subjected to sequence and structure-based analysis. Finally in this in-silico study, five deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) were identified in ACKR2(V41A), APOE(R176C), ATP8B4(G395S), LAMB2(E987K), and TOMM40(R239W), and these findings were subsequently backed-up by existing in-vivo and in-vitro literature. This study not only provides invaluable insight into the intricate pathogenic mechanisms underlying AD but also offers a distinctive perspective that paves the way for future, more comprehensive investigations aimed at unraveling the molecular intricacies responsible for the development and progression of AD. Nonetheless, it is imperative that further rigorous in vivo and in vitro experiments are conducted to validate and expand upon the findings presented here.

Shared Genetic Architecture Between Schizophrenia and Anorexia Nervosa: A Cross-trait Genome-Wide Analysis

BACKGROUND AND HYPOTHESIS

Schizophrenia (SCZ) and anorexia nervosa (AN) are 2 severe and highly heterogeneous disorders showing substantial familial co-aggregation. Genetic factors play a significant role in both disorders, but the shared genetic etiology between them is yet to be investigated.

STUDY DESIGN

Using summary statistics from recent large genome-wide association studies on SCZ (Ncases = 53 386) and AN (Ncases = 16 992), a 2-sample Mendelian randomization analysis was conducted to explore the causal relationship between SCZ and AN. MiXeR was employed to quantify their polygenic overlap. A conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was adopted to identify loci jointly associated with both disorders. Functional annotation and enrichment analyses were performed on the shared loci.

STUDY RESULTS

We observed a cross-trait genetic enrichment, a suggestive bidirectional causal relationship, and a considerable polygenic overlap (Dice coefficient = 62.2%) between SCZ and AN. The proportion of variants with concordant effect directions among all shared variants was 69.9%. Leveraging overlapping genetic associations, we identified 6 novel loci for AN and 33 novel loci for SCZ at condFDR <0.01. At conjFDR <0.05, we identified 10 loci jointly associated with both disorders, implicating multiple genes highly expressed in the cerebellum and pituitary and involved in synapse organization. Particularly, high expression of the shared genes was observed in the hippocampus in adolescence and orbitofrontal cortex during infancy.

CONCLUSIONS

This study provides novel insights into the relationship between SCZ and AN by revealing a shared genetic component and offers a window into their complex etiology.

Integration of multiple-omics data to reveal the shared genetic architecture of educational attainment, intelligence, cognitive performance, and Alzheimer's disease

Growing evidence suggests the effect of educational attainment (EA) on Alzheimer's disease (AD), but less is known about the shared genetic architecture between them. Here, leveraging genome-wide association studies (GWAS) for AD (N = 21,982/41,944), EA (N = 1,131,881), cognitive performance (N = 257,828), and intelligence (N = 78,308), we investigated their causal association with the linkage disequilibrium score (LDSC) and Mendelian randomization and their shared loci with the conjunctional false discovery rate (conjFDR), transcriptome-wide association studies (TWAS), and colocalization. We observed significant genetic correlations of EA (rg = -0.22, p = 5.07E-05), cognitive performance (rg = -0.27, p = 2.44E-05), and intelligence (rg = -0.30, p = 3.00E-04) with AD, and a causal relationship between EA and AD (OR = 0.74, 95% CI: 0.58-0.94, p = 0.013). We identified 13 shared loci at conjFDR <0.01, of which five were novel, and prioritized three causal genes. These findings inform early prevention strategies for AD.

Unveiling the Genetic Complexity of Teratozoospermia: Integrated Genomic Analysis Reveals Novel Insights into lncRNAs' Role in Male Infertility

Male infertility is a global health issue, affecting over 20 million men worldwide. Genetic factors are crucial in various male infertility forms, including teratozoospermia. Nonetheless, the genetic causes of male infertility remain largely unexplored. In this study, we employed whole-genome sequencing and RNA expression analysis to detect differentially expressed (DE) long-noncoding RNAs (lncRNAs) in teratozoospermia, along with mutations that are exclusive to teratozoospermic individuals within these DE lncRNAs regions. Bioinformatic tools were used to assess variants' impact on lncRNA structure, function, and lncRNA-miRNA interactions. Our analysis identified 1166 unique mutations in teratozoospermic men within DE lncRNAs, distinguishing them from normozoospermic men. Among these, 64 variants in 23 lncRNAs showed potential regulatory roles, 7 variants affected 4 lncRNA structures, while 37 variants in 17 lncRNAs caused miRNA target loss or gain. Pathway Enrichment and Gene Ontology analyses of the genes targeted by the affected miRNAs revealed dysregulated pathways in teratozoospermia and a link between male infertility and cancer. This study lists novel variants and lncRNAs associated for the first time with teratozoospermia. These findings pave the way for future studies aiming to enhance diagnosis and therapy in the field of male infertility.

No genetic causal association between Alzheimer's disease and osteoporosis: A bidirectional two-sample Mendelian randomization study

Objective

Many observational studies have found an association between Alzheimer's disease (AD) and osteoporosis. However, it is unclear whether there is causal genetic between osteoporosis and AD.

Methods

A two-sample Mendelian randomization (MR) study was used to investigate whether there is a causal relationship between osteoporosis and AD. Genes for osteoporosis and AD were obtained from published the genome-wide association studies (GWAS). Single nucleotide polymorphisms (SNPs) with significant genome-wide differences (p < 5 × 10^-8) and independent (r ² < 0.001) were selected, and SNPs with F ≥ 10 were further analyzed. Inverse variance weighted (IVW) was used to assess causality, and the results were reported as odds ratios (ORs). Subsequently, heterogeneity was tested using Cochran's Q test, pleiotropy was tested using the MR-Egger intercept, and leave-one-out sensitivity analysis was performed to assess the robustness of the results.

Results

Using the IVW method, MR Egger method, and median-weighted method, we found that the results showed no significant causal effect of osteoporosis at different sites and at different ages on AD, regardless of the removal of potentially pleiotropic SNPs. The results were similar for the opposite direction of causality. These results were confirmed to be reliable and stable by sensitivity analysis.

Conclusion

This study found that there is no bidirectional causal relationship between osteoporosis and AD. However, they share similar pathogenesis and pathways.

Genome-wide variants and polygenic risk scores for cognitive impairment following blood or marrow transplantation

Cognitive impairment is prevalent in blood or marrow transplantation (BMT) recipients, albeit with inter-individual variability. We conducted a genome-wide association study of objective cognitive function assessed longitudinally in 239 adult BMT recipients for discovery and replicated in an independent cohort of 540 BMT survivors. Weighted genome-wide polygenic risk scores (PRS) were constructed using linkage disequilibrium pruned significant SNPs. Forty-four genome-wide significant SNPs were identified using additive (n = 3); codominant (n = 20) and genotype models (n = 21). Each additional copy of a risk allele was associated with a 0.28-point (p = 1.07 × 10-8) to a 1.82-point (p = 6.7 × 10-12) increase in a global deficit score. We replicated two SNPs (rs11634183 and rs12486041) with links to neural integrity. Patients in the top PRS quintile were at increased risk of cognitive impairment in discovery (RR = 1.95, 95%CI: 1.28-2.96, p = 0.002) and replication cohorts (OR = 1.84, 95%CI, 1.02-3.32, p = 0.043). Associations were stronger among individuals with lowest clinical risk for cognitive impairment. These findings support potential utility of PRS-based risk classification in the development of targeted interventions aimed at improving cognitive outcomes in BMT survivors.

The Role of Atypical Chemokine Receptor D6 (ACKR2) in Physiological and Pathological Conditions; Friend, Foe, or Both?

Chemokines exert crucial roles in inducing immune responses through ligation to their canonical receptors. Besides these receptors, there are other atypical chemokine receptors (ACKR1–4) that can bind to a wide range of chemokines and carry out various functions in the body. ACKR2, due to its ability to bind various CC chemokines, has attracted much attention during the past few years. ACKR2 has been shown to be expressed in different cells, including trophoblasts, myeloid cells, and especially lymphoid endothelial cells. In terms of molecular functions, ACKR2 scavenges various inflammatory chemokines and affects inflammatory microenvironments. In the period of pregnancy and fetal development, ACKR2 plays a pivotal role in maintaining the fetus from inflammatory reactions and inhibiting subsequent abortion. In adults, ACKR2 is thought to be a resolving agent in the body because it scavenges chemokines. This leads to the alleviation of inflammation in different situations, including cardiovascular diseases, autoimmune diseases, neurological disorders, and infections. In cancer, ACKR2 exerts conflicting roles, either tumor-promoting or tumor-suppressing. On the one hand, ACKR2 inhibits the recruitment of tumor-promoting cells and suppresses tumor-promoting inflammation to blockade inflammatory responses that are favorable for tumor growth. In contrast, scavenging chemokines in the tumor microenvironment might lead to disruption in NK cell recruitment to the tumor microenvironment. Other than its involvement in diseases, analyzing the expression of ACKR2 in body fluids and tissues can be used as a biomarker for diseases. In conclusion, this review study has tried to shed more light on the various effects of ACKR2 on different inflammatory conditions.

Systematic review of genome-wide association studies of abdominal aortic aneurysm

BACKGROUND AND AIMS

Abdominal aortic aneurysm (AAA) is an important cause of death worldwide and has an estimated heritability between 70 and 77%. Genome-wide association studies (GWAS) are an established way to discover genetic risk variants. The aim of this study was to systematically review the findings and quality of previous AAA GWAS.

METHODS

The Medline, PubMed, Web of Science and relevant genetic databases were searched to identify previous AAA GWAS. A framework was developed to grade the methodological quality of the GWAS. Data from included studies were extracted to assess methods and findings.

RESULTS

Eight case-control studies were included. Thirty-three of the 38 total single nucleotide polymorphisms (SNPs) previously reported were associated with AAA diagnosis at genome-wide significance (p < 5.0 × 10^-8). The CDKN2B antisense RNA-1 gene had the most significant association with AAA diagnosis (p = 6.94 × 10^-29 and p = 1.54 × 10^-33 for rs4007642 and rs10757274 respectively). Age, sex and smoking history were not reported for the complete cohort in any of the included studies, although five of the eight studies adjusted or matched for at least two confounding variables. All included studies had important design limitations including lack of sample size estimation, inconsistent case and control ascertainment and limited phenotyping of the AAAs. AAA growth was assessed in one GWAS, however, no significant associations with the reported SNPs were found.

CONCLUSIONS

This systematic review identified 33 SNPs associated with AAA diagnosis at genome-wide significance previously validated in multiple cohorts. The association between SNPs and AAA growth was not adequately examined. Previous GWAS have a number of design limitations.

Genetic association of BIN1 and GAB2 in Alzheimer's disease: A meta-analysis and systematic review

Aim

Heredity plays an important role in the pathogenesis of Alzheimer's disease (AD) especially for single‐nucleotide polymorphism (SNPs) of susceptible genes, which is one of the significant factors in the pathogenesis of AD. The SNPs of BIN1 rs744373, BIN1 rs7561528 and GAB2 rs2373115 are associated with AD in Asian and white people.

Methods

We included 34 studies with a total of 38 291 patients with AD and 55 538 controls of diverse races from four main databases. We used meta‐analysis to obtain I²‐values and odds ratios of five genetic models in three SNPs. We carried out analysis of sensitivity, subgroup, publication bias and linkage disequilibrium test.

Results

The forest plots showed the odds ratio value of the three SNPs was >1 in white individuals, but not Asian individuals, in their genetic model. The funnel plot was symmetrical, and the D'‐value was 0.986 between rs744373 and rs7561528.

Conclusions

BIN1 rs744373, BIN1 rs7561528 and GAB2 rs2373115 are pathogenicity sites for AD in white people, and also rs7561528 belongs to a risk site in Asian people. The rs7561528 and rs744373 SNPs have strong linkage disequilibrium in Chinese people. In addition, apolipoprotein E ε4 status promotes them to result in the pathogenesis of AD. Geriatr Gerontol Int 2021; 21: 185–191.

A Genome-Wide Study of Single-Nucleotide Polymorphisms in MicroRNAs and Further In Silico Analysis Reveals Their Putative Role in Susceptibility to Late-Onset Alzheimer's Disease

Late-onset Alzheimer's disease (LOAD) is a neurodegenerative disorder of growing relevance in an aging society for which predictive biomarkers are needed. Many genes involved in LOAD are tightly controlled by microRNAs (miRNAs), which can be modulated by single-nucleotide polymorphisms (SNPs). Our aim was to determine the association between SNPs in miRNAs and LOAD. We selected all SNPs in pre-miRNAs with a minor allele frequency (MAF) > 1% and genotyped them in a cohort of 229 individuals diagnosed with LOAD and 237 unrelated healthy controls. In silico analyses were performed to predict the effect of SNPs on miRNA stability and detect downstream pathways. Four SNPs were associated with LOAD risk with a p value < 0.01 (rs74704964 in hsa-miR-518d, rs71363366 in hsa-miR-1283-2, rs11983381 in hsa-miR-4653, and rs10934682 in hsa-miR-544b). In silico analyses support a possible functional effect of those SNPs in miRNA levels and in the regulation of pathways of relevance for the development of LOAD. Although the results are promising, additional studies are needed to validate the association between SNPs in miRNAs and the risk of developing LOAD. Graphical abstract.

Molecular linkage between post-traumatic stress disorder and cognitive impairment: a targeted proteomics study of World Trade Center responders

Existing work on proteomics has found common biomarkers that are altered in individuals with post-traumatic stress disorder (PTSD) and mild cognitive impairment (MCI). The current study expands our understanding of these biomarkers by profiling 276 plasma proteins with known involvement in neurobiological processes using the Olink Proseek Multiplex Platform in individuals with both PTSD and MCI compared to either disorder alone and with unaffected controls. Participants were World Trade Center (WTC) responders recruited through the Stony Brook WTC Health Program. PTSD and MCI were measured with the PTSD Checklist (PCL) and the Montreal Cognitive Assessment, respectively. Compared with unaffected controls, we identified 16 proteins associated with comorbid PTSD-MCI at P < 0.05 (six at FDR < 0.1), 20 proteins associated with PTSD only (two at FDR < 0.1), and 24 proteins associated with MCI only (one at FDR < 0.1), for a total of 50 proteins. The multiprotein composite score achieved AUCs of 0.84, 0.77, and 0.83 for PTSD-MCI, PTSD only, and MCI only versus unaffected controls, respectively. To our knowledge, the current study is the largest to profile a large set of proteins involved in neurobiological processes. The significant associations across the three case-group analyses suggest that shared biological mechanisms may be involved in the two disorders. If findings from the multiprotein composite score are replicated in independent samples, it has the potential to add a new tool to help classify both PTSD and MCI.

Atypical chemokine receptor ACKR2-V41A has decreased CCL2 binding, scavenging, and activation, supporting sustained inflammation and increased Alzheimer's disease risk

A recent genome-wide association study (GWAS) of 59 cerebrospinal fluid (CSF) proteins with a connection to Alzheimer's disease (AD) demonstrated an association between increased levels of chemokine ligand 2 (CCL2) with an atypical chemokine receptor chemokine-binding protein 2 variant V41A (ACKR2-V41A; rs2228467). High levels of CCL2 are associated with increased risk of AD development as well as other inflammatory diseases. In this study we characterized the biological function of the ACKR2-V41A receptor compared to the wild type allele by measuring its ligand binding affinity, CCL2 scavenging efficiency, and cell activation sensitivity. We transfected Chinese hamster ovary cells with plasmids carrying wild type ACKR2 (ACKR2-WT) or the mutant ACKR2-V41A receptor. Binding affinity assays showed that ACKR2-V41A has a lower binding affinity for CCL2 and CCL4 than ACKR2-WT. CCL2 scavenging results aligned with binding affinity assays, with ACKR2-V41A cells scavenging CCL2 with a lower efficiency than ACKR2-WT. Cell activation assays also showed that ACKR2-V41A cells had significantly lower receptor upregulation (β-Arrestin-dependent signaling pathway) upon stimulation compared to ACKR2-WT cells. These findings provide molecular and biological mechanistic insights into the GWAS association of ACKR2-V41A with increased levels of CCL2 in CSF and possibly other chemokine ligands. Increased CCL2 levels are associated with accelerated cognitive decline and increased risk of AD. Understanding how this atypical chemokine receptor allele increases serum markers of inflammation could lead to novel therapeutic solutions for AD.

"Amyloid-beta accumulation cycle" as a prevention and/or therapy target for Alzheimer's disease

The cell cycle and its regulators are validated targets for cancer drugs. Reagents that target cells in a specific cell cycle phase (e.g., antimitotics or DNA synthesis inhibitors/replication stress inducers) have demonstrated success as broad-spectrum anticancer drugs. Cyclin-dependent kinases (CDKs) are drivers of cell cycle transitions. A CDK inhibitor, flavopiridol/alvocidib, is an FDA-approved drug for acute myeloid leukemia. Alzheimer's disease (AD) is another serious issue in contemporary medicine. The cause of AD remains elusive, although a critical role of latent amyloid-beta accumulation has emerged. Existing AD drug research and development targets include amyloid, amyloid metabolism/catabolism, tau, inflammation, cholesterol, the cholinergic system, and other neurotransmitters. However, none have been validated as therapeutically effective targets. Recent reports from AD-omics and preclinical animal models provided data supporting the long-standing notion that cell cycle progression and/or mitosis may be a valid target for AD prevention and/or therapy. This review will summarize the recent developments in AD research: (a) Mitotic re-entry, leading to the "amyloid-beta accumulation cycle," may be a prerequisite for amyloid-beta accumulation and AD pathology development; (b) AD-associated pathogens can cause cell cycle errors; (c) thirteen among 37 human AD genetic risk genes may be functionally involved in the cell cycle and/or mitosis; and (d) preclinical AD mouse models treated with CDK inhibitor showed improvements in cognitive/behavioral symptoms. If the "amyloid-beta accumulation cycle is an AD drug target" concept is proven, repurposing of cancer drugs may emerge as a new, fast-track approach for AD management in the clinic setting.

PICALM Gene Methylation in Blood of Alzheimer's Disease Patients Is Associated with Cognitive Decline

Epigenetic mechanisms might be involved in Alzheimer's disease (AD). Genetic polymorphisms in several genes, including APOE (Apolipoprotein E), PSEN1 (Presenilin 1), CR1 (Complement receptor 1), and PICALM (Phosphatidylinositol binding clathrin assembly protein), have been associated to an increased AD risk. However, data regarding methylation of these specific genes are lacking. We evaluated DNA methylation measured by quantitative bisulfite-PCR pyrosequencing in 43 AD patients and 38 healthy subjects (HS). In a multivariate age- and gender-adjusted model, PICALM methylation was decreased in AD compared to HS (mean = 3.54 and 4.63, respectively, p = 0.007). In AD, PICALM methylation level was also positively associated to Mini-Mental Scale Examination (MMSE) score (percent change 3.48%, p = 0.008). Moreover, a negative association between PICALM methylation and age was observed only in HS (percent change - 2.29%, p = 0.002). In conclusion, our data suggest a possible role of PICALM methylation in AD, particularly related to cognitive function. Given the small study sample and the associative nature of our study, further prospective investigations are required to assess the dynamics of DNA methylation in the early stages of AD development.

Innovative strategies for annotating the "relationSNP" between variants and molecular phenotypes

Characterizing how variation at the level of individual nucleotides contributes to traits and diseases has been an area of growing interest since the completion of sequencing the first human genome. Our understanding of how a single nucleotide polymorphism (SNP) leads to a pathogenic phenotype on a genome-wide scale is a fruitful endeavor for anyone interested in developing diagnostic tests, therapeutics, or simply wanting to understand the etiology of a disease or trait. To this end, many datasets and algorithms have been developed as resources/tools to annotate SNPs. One of the most common practices is to annotate coding SNPs that affect the protein sequence. Synonymous variants are often grouped as one type of variant, however there are in fact many tools available to dissect their effects on gene expression. More recently, large consortiums like ENCODE and GTEx have made it possible to annotate non-coding regions. Although annotating variants is a common technique among human geneticists, the constant advances in tools and biology surrounding SNPs requires an updated summary of what is known and the trajectory of the field. This review will discuss the history behind SNP annotation, commonly used tools, and newer strategies for SNP annotation. Additionally, we will comment on the caveats that distinguish approaches from one another, along with gaps in the current state of knowledge, and potential future directions. We do not intend for this to be a comprehensive review for any specific area of SNP annotation, but rather it will be an excellent resource for those unfamiliar with computational tools used to functionally characterize SNPs. In summary, this review will help illustrate how each SNP annotation method impacts the way in which the genetic and molecular etiology of a disease is explored in-silico.

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.

Cortical astroglia undergo transcriptomic dysregulation in the G93A SOD1 ALS mouse model

Astroglia are the most abundant glia cell in the central nervous system, playing essential roles in maintaining homeostasis. Key functions of astroglia include, but are not limited to, neurotransmitter recycling, ion buffering, immune modulation, neurotrophin secretion, neuronal synaptogenesis and elimination, and blood-brain barrier maintenance. In neurological diseases, it is well appreciated that astroglia play crucial roles in the disease pathogenesis. In amyotrophic lateral sclerosis (ALS), a motor neuron degenerative disease, astroglia in the spinal cord and cortex downregulate essential transporters, among other proteins, that exacerbate disease progression. Spinal cord astroglia undergo dramatic transcriptome dysregulation. However, in the cortex, it has not been well studied what effects glia, especially astroglia, have on upper motor neurons in the pathology of ALS. To begin to shed light on the involvement and dysregulation that astroglia undergo in ALS, we isolated pure grey-matter cortical astroglia and subjected them to microarray analysis. We uncovered a vast number of genes that show dysregulation at end-stage in the ALS mouse model, G93A SOD1. Many of these genes play essential roles in ion homeostasis and the Wnt-signaling pathway. Several of these dysregulated genes are common in ALS spinal cord astroglia, while many of them are unique. This database serves as an approach for understanding the significance of dysfunctional genes and pathways in cortical astroglia in the context of motor neuron disease, as well as determining regional astroglia heterogeneity, and providing insight into ALS pathogenesis.

Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles

The identification of disease-related genes and disease mechanisms is an important research goal; many studies have approached this problem by analysing genetic networks based on gene expression profiles and interaction datasets. To construct a gene network, correlations or associations among pairs of genes must be obtained. However, when gene expression data are heterogeneous with high levels of noise for samples assigned to the same condition, it is difficult to accurately determine whether a gene pair represents a significant gene-gene interaction (GGI). In order to solve this problem, we proposed a random forest-based method to classify significant GGIs from gene expression data. To train the model, we defined novel feature sets and utilised various high-confidence interactome datasets to deduce the correct answer set from known disease-specific genes. Using Alzheimer's disease data, the proposed method showed remarkable accuracy, and the GGIs established in the analysis can be used to build a meaningful genetic network that can explain the mechanisms underlying Alzheimer's disease.

Computational analyses of obesity associated loci generated by genome-wide association studies

OBJECTIVES

Genome-wide association studies (GWASs) have discovered associations of numerous SNPs and genes with obesity. However, the underlying molecular mechanisms through which these SNPs and genes affect the predisposition to obesity remain not fully understood. Aims of our study are to comprehensively characterize obesity GWAS SNPs and genes through computational approaches.

METHODS

For obesity GWAS identified SNPs, functional annotation, effects on miRNAs binding and impact on protein phosphorylation were performed via RegulomeDB and 3DSNP, miRNASNP, and the PhosSNP 1.0 database, respectively. For obesity associated genes, protein-protein interaction network construction, gene ontology and pathway enrichment analyses were performed by STRING, PANTHER and STRING, respectively.

RESULTS

A total of 445 SNPs are significantly associated with obesity related phenotypes at threshold P < 5×10-8. A number of SNPs were eQTLs for obesity associated genes, some SNPs located at binding sites of obesity related transcription factors. SNPs that might affect miRNAs binding and protein phosphorylation were identified. Protein-protein interaction network analysis identified the highly-interconnected "hub" genes. Obesity associated genes mainly involved in metabolic process and catalytic activity, and significantly enriched in 15 signal pathways.

CONCLUSIONS

Our results provided the targets for follow-up experimental testing and further shed new light on obesity pathophysiology.

Mutation burden profile in familial Alzheimer's disease cases from India

This study attempts to identify coding risk variants in genes previously implicated in Alzheimer's disease (AD) pathways, through whole-exome sequencing of subjects (N = 17) with AD, with a positive family history of dementia (familial AD). We attempted to evaluate the mutation burden in genes encoding amyloid precursor protein metabolism and previously linked to risk of dementias. Novel variants were identified in genes involved in amyloid precursor protein metabolism such as PSEN1 (chr 14:73653575, W161C, tgg > tgT), PLAT (chr 8:42039530,G272R), and SORL1 (chr11:121414373,G601D). The mutation burden assessment of dementia-related genes for all 17 cases revealed 45 variants, which were either shared across subjects, or were present in just the 1 patient. The study shows that the clinical characteristics, and genetic correlates, obtained in this sample are broadly comparable to the other studies that have investigated familial forms of AD. Our study identifies rare deleterious genetic variations, in the coding region of genes involved in amyloid signaling, and other dementia-associated pathways.

The association of MPO gene promoter polymorphisms with Alzheimer's disease risk in Chinese Han population

Aim

The objective of this study was to explore the genetic association of myeloperoxidase (MPO) gene polymorphisms with risk of Alzheimer's disease (AD).

Methods

Blood samples were collected from 116 AD patients and 134 age and gender matched healthy individuals. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was utilized to confirm MPO polymorphisms in promoter region. Plasma concentration of MPO was detected by enzyme-linked immuno sorbent assay. Genotype distributions of MPO polymorphisms were compared by χ² test between the two groups. The status of linkage disequilibrium between MPO two polymorphisms was detected using Haploview. MPO concentrations were analyzed by non-parametric test.

Results

MPO rs2333227 polymorphism was positively associated with AD risk, especially under the AA+GA vs. GG and A vs. G genetic models (P=0.042, OR=1.719, 95%CI=1.017-2.906; P=0.041, OR=1.582, 95%CI=1.016-2.463). While, rs34097845 polymorphism significantly decreased the risk of AD, particularly GA and AA+GA genotypes (P=0.048, OR=0.555, 95%CI=0.308-0.998; P=0.042, OR=0.552, 95%CI=0.310-0.983). In addition, rs2333227 genotypes affected the plasma concentration of MPO. But for rs34097845 polymorphism, only GA genotype exhibited significant association with MPO concentration.

Conclusion

Polymorphisms in the promoter region of MPO distinctly contribute to AD risk possibly through regulating MPO concentration. Present results should be confirmed by further studies.