Genetic loci associated with Alzheimer's disease

MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies

Traditional predictive models for transcriptome-wide association studies (TWAS) consider only single nucleotide polymorphisms (SNPs) local to genes of interest and perform parameter shrinkage with a regularization process. These approaches ignore the effect of distal-SNPs or other molecular effects underlying the SNP-gene association. Here, we outline multi-omics strategies for transcriptome imputation from germline genetics to allow more powerful testing of gene-trait associations by prioritizing distal-SNPs to the gene of interest. In one extension, we identify mediating biomarkers (CpG sites, microRNAs, and transcription factors) highly associated with gene expression and train predictive models for these mediators using their local SNPs. Imputed values for mediators are then incorporated into the final predictive model of gene expression, along with local SNPs. In the second extension, we assess distal-eQTLs (SNPs associated with genes not in a local window around it) for their mediation effect through mediating biomarkers local to these distal-eSNPs. Distal-eSNPs with large indirect mediation effects are then included in the transcriptomic prediction model with the local SNPs around the gene of interest. Using simulations and real data from ROS/MAP brain tissue and TCGA breast tumors, we show considerable gains of percent variance explained (1-2% additive increase) of gene expression and TWAS power to detect gene-trait associations. This integrative approach to transcriptome-wide imputation and association studies aids in identifying the complex interactions underlying genetic regulation within a tissue and important risk genes for various traits and disorders.

TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits

The transcriptome-wide association studies (TWASs) that test for association between the study trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. By using the gene expression imputation models fitted from reference datasets that have both genetic and transcriptomic data, TWASs facilitate gene-based tests with GWAS data while accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and FUSION use parametric imputation models that have limitations for modeling the complex genetic architecture of transcriptomic data. Therefore, to improve on this, we employ a nonparametric Bayesian method that was originally proposed for genetic prediction of complex traits, which assumes a data-driven nonparametric prior for cis-eQTL effect sizes. The nonparametric Bayesian method is flexible and general because it includes both of the parametric imputation models used by PrediXcan and FUSION as special cases. Our simulation studies showed that the nonparametric Bayesian model improved both imputation R2 for transcriptomic data and the TWAS power over PrediXcan when ≥1% cis-SNPs co-regulate gene expression and gene expression heritability ≤0.2. In real applications, the nonparametric Bayesian method fitted transcriptomic imputation models for 57.8% more genes over PrediXcan, thus improving the power of follow-up TWASs. We implement both parametric PrediXcan and nonparametric Bayesian methods in a convenient software tool "TIGAR" (Transcriptome-Integrated Genetic Association Resource), which imputes transcriptomic data and performs subsequent TWASs using individual-level or summary-level GWAS data.

Careful neuropsychological testing reveals a novel genetic marker, GSTO1*C, linked to the pre-stage of Alzheimer's disease

Approximately 30 million people currently suffer from late-onset Alzheimer's disease (LOAD) worldwide. Twin studies demonstrated that 60 to 80% of LOAD is genetically determined, 20% of which remaining unassigned. This case-control study included 118 cognitively healthy controls, 52 patients with mild cognitive impairment (MCI; the pre-stage of LOAD) and 71 LOAD patients. The participants were genotyped for the genetic LOAD marker apolipoprotein E4 (APOE4) and the single-nucleotide polymorphism rs4925 in glutathione S-transferase omega-1 (GSTO1). Additive logistic regression showed a novel, statistically significant association of the major allele GSTO1*C with MCI (OR1.9; p = 0.032). However, identification of significant SNP-disease relations required well-defined study groups. When classifying participants solely by the short Mini Mental State examination (MMSE), the associations of GSTO1*C and the reference marker APOE4 with MCI were cancelled. Moreover, even identifying only the control group by MMSE nullified a statistically significant association (OR1.8; p = 0.045) between GSTO1*C and LOAD. In contrast, these statistical relations were retained when the detailed Consortium to Establish a Registry for Alzheimer's Disease (CERAD-Plus) test battery was used. Hence, besides proposing rs4925 as a genetic marker for cognitive impairment, this work also emphasized the importance of carefully characterized controls in addition to well-diagnosed patients in case-control studies.

Genetics of Alzheimer's disease: an update

It is clear that late-onset Alzheimer's disease (AD), the most common form of dementia in western societies, has a significant genetic component. The recent technological advances in high-throughput genome technologies have enabled the identification of more than 20 novel susceptibility loci. These findings have significantly advanced the understanding of the molecular mechanisms potentially underlying AD etiology, and have therefore provided valuable information for the development of targets for genetic testing, prevention and treatment. This article reviews these recent findings in AD genomics and discusses their implications for understanding the molecular underpinnings of the disease.

A Meta-Analysis on the Relationship of the PON Genes and Alzheimer Disease

AIM

This study aimed to evaluate the association of the paraoxonase (PON) gene variants and Alzheimer disease (AD) using meta-analysis.

METHODS

Relevant studies were identified by searching English and Chinese databases extensively. Allele and genotype frequencies for each included study were extracted. Newcastle-Ottawa Scale (NOS) was employed to assess the quality of included studies. The odds ratio (OR) with 95% confidence interval (95% CI) was calculated using a random-effects or fixed-effects model. A Q statistic was used to evaluate homogeneity, and Egger test and funnel plot were used to assess publication bias.

RESULTS

A total of 15 studies (involving 5 polymorphisms) were included and identified for the current meta-analysis. The NOS scores ranged from 7 to 8, meaning good quality of studies. It was found that the SS genotype of PON2 S311C polymorphism had an significant association with AD in the studied population (OR = 0.82, 95% CI: 0.68-0.99, P = .04), and the A allele of PON1 rs705379 polymorphism was positively related to AD in Caucasian population (OR = 1.21, 95% CI: 1.05-1.39, P = .009) as well as the GG genotype decreased AD risk significantly in Caucasians (OR = 0.7, 95% CI: 0.56-0.88, P = .002). However, there was no significant relationship between other 3 genetic variants of PON genes (L55 M, Q192 R, and -161C/T of PON1 gene) and AD.

CONCLUSION

Existing evidence indicates that the S311C polymorphism (SS genotype) and the rs705379 (the A allele and GG genotype) are associated with risk of AD in studied population. Future studies with larger sample sizes will be necessary to confirm the present results.

Altered protein phosphorylation as a resource for potential AD biomarkers

The amyloidogenic peptide, Aβ, provokes a series of events affecting distinct cellular pathways regulated by protein phosphorylation. Aβ inhibits protein phosphatases in a dose-dependent manner, thus it is expected that the phosphorylation state of specific proteins would be altered in response to Aβ. In fact several Alzheimer’s disease related proteins, such as APP and TAU, exhibit pathology associated hyperphosphorylated states. A systems biology approach was adopted and the phosphoproteome, of primary cortical neuronal cells exposed to Aβ, was evaluated. Phosphorylated proteins were recovered and those whose recovery increased or decreased, upon Aβ exposure across experimental sets, were identified. Significant differences were evident for 141 proteins and investigation of their interactors revealed key protein clusters responsive to Aβ treatment. Of these, 73 phosphorylated proteins increased and 68 decreased upon Aβ addition. These phosphorylated proteins represent an important resource of potential AD phospho biomarkers that should be further pursued.

Toward precision medicine in Alzheimer's disease

In Western societies, Alzheimer's disease (AD) is the most common form of dementia and the sixth leading cause of death. In recent years, the concept of precision medicine, an approach for disease prevention and treatment that is personalized to an individual's specific pattern of genetic variability, environment and lifestyle factors, has emerged. While for some diseases, in particular select cancers and a few monogenetic disorders such as cystic fibrosis, significant advances in precision medicine have been made over the past years, for most other diseases precision medicine is only in its beginning. To advance the application of precision medicine to a wider spectrum of disorders, governments around the world are starting to launch Precision Medicine Initiatives, major efforts to generate the extensive scientific knowledge needed to integrate the model of precision medicine into every day clinical practice. In this article we summarize the state of precision medicine in AD, review major obstacles in its development, and discuss its benefits in this highly prevalent, clinically and pathologically complex disease.

Genetic and Transcriptomic Profiles of Inflammation in Neurodegenerative Diseases: Alzheimer, Parkinson, Creutzfeldt-Jakob and Tauopathies

Polymorphisms in certain inflammatory-related genes have been identified as putative differential risk factors of neurodegenerative diseases with abnormal protein aggregates, such as sporadic Alzheimer’s disease (AD) and sporadic Parkinson’s disease (sPD). Gene expression studies of cytokines and mediators of the immune response have been made in post-mortem human brain samples in AD, sPD, sporadic Creutzfeldt-Jakob disease (sCJD) subtypes MM1 and VV2, Pick’s disease (PiD), progressive supranuclear palsy (PSP) and frontotemporal lobar degeneration linked to mutation P301L in MAPT Frontotemporal lobar degeneration-tau (FTLD-tau). The studies have disclosed variable gene regulation which is: (1) disease-dependent in the frontal cortex area 8 in AD, sPD, sCJD MM1 and VV2, PiD, PSP and FTLD-tau; (2) region-dependent as seen when comparing the entorhinal cortex, orbitofrontal cortex, and frontal cortex area 8 (FC) in AD; the substantia nigra, putamen, FC, and angular gyrus in PD, as well as the FC and cerebellum in sCJD; (3) genotype-dependent as seen considering sCJD MM1 and VV2; and (4) stage-dependent as seen in AD at different stages of disease progression. These observations show that regulation of inflammation is much more complicated and diverse than currently understood, and that new therapeutic approaches must be designed in order to selectively act on specific targets in particular diseases and at different time points of disease progression.