High-resolution whole-genome association study of Parkinson disease

Assessing the reproducibility of machine-learning-based biomarker discovery in Parkinson's disease

Feature selection and machine learning algorithms can be used to analyze Single Nucleotide Polymorphisms (SNPs) data and identify potential disease biomarkers. Reproducibility of identified biomarkers is critical for them to be useful for clinical research; however, genotyping platforms and selection criteria for individuals to be genotyped affect the reproducibility of identified biomarkers. To assess biomarkers reproducibility, we collected five SNPs datasets from the database of Genotypes and Phenotypes (dbGaP) and explored several data integration strategies. While combining datasets can lead to a reduction in classification accuracy, it has the potential to improve the reproducibility of potential biomarkers. We evaluated the agreement among different strategies in terms of the SNPs that were identified as potential Parkinson's disease (PD) biomarkers. Our findings indicate that, on average, 93% of the SNPs identified in a single dataset fail to be identified in other datasets. However, through dataset integration, this lack of replication is reduced to 62%. We discovered fifty SNPs that were identified at least twice, which could potentially serve as novel PD biomarkers. These SNPs are indirectly linked to PD in the literature but have not been directly associated with PD before. These findings open up new potential avenues of investigation.

Multi-ancestry genome-wide association meta-analysis of Parkinson's disease

Although over 90 independent risk variants have been identified for Parkinson's disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson's disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations.

Genetic Architecture of Parkinson's Disease

Year 2022 marks 25 years since the first mutation in familial autosomal dominant Parkinson's disease was identified. Over the years, our understanding of the role of genetic factors in the pathogenesis of familial and idiopathic forms of Parkinson's disease has expanded significantly - a number of genes for the familial form of the disease have been identified, and DNA markers for an increased risk of developing its sporadic form have been found. But, despite all the success achieved, we are far from an accurate assessment of the contribution of genetic and, even more so, epigenetic factors to the disease development. The review summarizes the information accumulated to date on the genetic architecture of Parkinson's disease and formulates issues that need to be addressed, which are primarily related to the assessment of epigenetic factors in the disease pathogenesis.

The Big Picture of Neurodegeneration: A Meta Study to Extract the Essential Evidence on Neurodegenerative Diseases in a Network-Based Approach

The common features of all neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis (ALS), and Huntington's disease, are the accumulation of aggregated and misfolded proteins and the progressive loss of neurons, leading to cognitive decline and locomotive dysfunction. Still, they differ in their ultimate manifestation, the affected brain region, and the kind of proteinopathy. In the last decades, a vast number of processes have been described as associated with neurodegenerative diseases, making it increasingly harder to keep an overview of the big picture forming from all those data. In this meta-study, we analyzed genomic, transcriptomic, proteomic, and epigenomic data of the aforementioned diseases using the data of 234 studies in a network-based approach to study significant general coherences but also specific processes in individual diseases or omics levels. In the analysis part, we focus on only some of the emerging findings, but trust that the meta-study provided here will be a valuable resource for various other researchers focusing on specific processes or genes contributing to the development of neurodegeneration.

The Parkinson's disease protein alpha-synuclein is a modulator of processing bodies and mRNA stability

Alpha-synuclein (αS) is a conformationally plastic protein that reversibly binds to cellular membranes. It aggregates and is genetically linked to Parkinson's disease (PD). Here, we show that αS directly modulates processing bodies (P-bodies), membraneless organelles that function in mRNA turnover and storage. The N terminus of αS, but not other synucleins, dictates mutually exclusive binding either to cellular membranes or to P-bodies in the cytosol. αS associates with multiple decapping proteins in close proximity on the Edc4 scaffold. As αS pathologically accumulates, aberrant interaction with Edc4 occurs at the expense of physiologic decapping-module interactions. mRNA decay kinetics within PD-relevant pathways are correspondingly disrupted in PD patient neurons and brain. Genetic modulation of P-body components alters αS toxicity, and human genetic analysis lends support to the disease-relevance of these interactions. Beyond revealing an unexpected aspect of αS function and pathology, our data highlight the versatility of conformationally plastic proteins with high intrinsic disorder.

A Sparse Mixture-of-Experts Model With Screening of Genetic Associations to Guide Disease Subtyping

Motivation: Identifying new genetic associations in non-Mendelian complex diseases is an increasingly difficult challenge. These diseases sometimes appear to have a significant component of heritability requiring explanation, and this missing heritability may be due to the existence of subtypes involving different genetic factors. Taking genetic information into account in clinical trials might potentially have a role in guiding the process of subtyping a complex disease. Most methods dealing with multiple sources of information rely on data transformation, and in disease subtyping, the two main strategies used are 1) the clustering of clinical data followed by posterior genetic analysis and 2) the concomitant clustering of clinical and genetic variables. Both of these strategies have limitations that we propose to address. Contribution: This work proposes an original method for disease subtyping on the basis of both longitudinal clinical variables and high-dimensional genetic markers via a sparse mixture-of-regressions model. The added value of our approach lies in its interpretability in relation to two aspects. First, our model links both clinical and genetic data with regard to their initial nature (i.e., without transformation) and does not require post-processing where the original information is accessed a second time to interpret the subtypes. Second, it can address large-scale problems because of a variable selection step that is used to discard genetic variables that may not be relevant for subtyping. Results: The proposed method was validated on simulations. A dataset from a cohort of Parkinson's disease patients was also analyzed. Several subtypes of the disease and genetic variants that potentially have a role in this typology were identified. Software availability: The R code for the proposed method, named DiSuGen, and a tutorial are available for download (see the references).

SMetABF: A rapid algorithm for Bayesian GWAS meta-analysis with a large number of studies included

Bayesian methods are widely used in the GWAS meta-analysis. But the considerable consumption in both computing time and memory space poses great challenges for large-scale meta-analyses. In this research, we propose an algorithm named SMetABF to rapidly obtain the optimal ABF in the GWAS meta-analysis, where shotgun stochastic search (SSS) is introduced to improve the Bayesian GWAS meta-analysis framework, MetABF. Simulation studies confirm that SMetABF performs well in both speed and accuracy, compared to exhaustive methods and MCMC. SMetABF is applied to real GWAS datasets to find several essential loci related to Parkinson's disease (PD) and the results support the underlying relationship between PD and other autoimmune disorders. Developed as an R package and a web tool, SMetABF will become a useful tool to integrate different studies and identify more variants associated with complex traits.

Prevalence of Transthyretin Amyloid Cardiomyopathy in Heart Failure With Preserved Ejection Fraction

Importance

Heart failure (HF) with preserved ejection fraction (HFpEF) is common, is frequently associated with ventricular wall thickening, and has no effective therapy. Transthyretin amyloid cardiomyopathy (ATTR-CM) can cause the HFpEF clinical phenotype, has highly effective therapy, and is believed to be underrecognized.

Objective

To examine the prevalence of ATTR-CM without and with systematic screening in patients with HFpEF and ventricular wall thickening.

Design, Setting, and Participants

This population-based cohort study assessed ATTR-CM prevalence in 1235 consecutive patients in southeastern Minnesota with HFpEF both without (prospectively identified cohort study) and with (consenting subset of cohort study, n = 286) systematic screening. Key entry criteria included validated HF diagnosis, age of 60 years or older, ejection fraction of 40% or greater, and ventricular wall thickness of 12 mm or greater. In this community cohort of 1235 patients, 884 had no known ATTR-CM, contraindication to technetium Tc 99m pyrophosphate scanning, or other barriers to participation in the screening study. Of these 884 patients, 295 consented and 286 underwent scanning between October 5, 2017, and March 9, 2020 (community screening cohort).

Exposures

Medical record review or technetium Tc 99m pyrophosphate scintigraphy and reflex testing for ATTR-CM diagnosis.

Main Outcomes and Measures

The ATTR-CM prevalence by strategy (clinical diagnosis or systematic screening), age, and sex.

Results

A total of 1235 patients participated in the study, including a community cohort (median age, 80 years; interquartile range, 72-87 years; 630 [51%] male) and a community screening cohort (n = 286; median age, 78 years; interquartile range, 71-84 years; 149 [52%] male). In the 1235 patients in the community cohort without screening group, 16 patients (1.3%; 95% CI, 0.7%-2.1%) had clinically recognized ATTR-CM. The prevalence was 2.5% (95% CI, 1.4%-4.0%) in men and 0% (95% CI, 0.0%-0.6%) in women. In the 286 patients in the community screening cohort, 18 patients (6.3%; 95% CI, 3.8%-9.8%) had ATTR-CM. Prevalence increased with age from 0% in patients 60 to 69 years of age to 21% in patients 90 years and older (P < .001). Adjusting for age, ATTR-CM prevalence differed by sex, with 15 of 149 men (10.1%; 95% CI, 5.7%-16.1%) and 3 of 137 women (2.2%; 95% CI, 0.4%-6.3%) having ATTR-CM (P = .002).

Conclusions and Relevance

In this cohort study based in a community-based setting, ATTR-CM was present in a substantial number of cases of HFpEF with ventricular wall thickening, particularly in older men. These results suggest that systematic evaluation can increase the diagnosis of ATTR-CM, thereby providing therapeutically relevant phenotyping of HFpEF.

Replication of chromosomal loci involved in Parkinson's disease: A quantitative synthesis of GWAS

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The first quantitative synthesis of GWAS regarding Parkinson’s Disease.

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Fifteen Parkinson’s Disease GWASs with 191.397 available SNPs pooled.

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User friendly software (METRADISC-XL) implemented.

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Seven chromosomal regions (bins) were replicated as associated with the Parkinson’s Disease trait.

Introduction

Parkinson’s disease is a neurodegenerative disorder with a complex etiology coming from interactions between genetic and environmental factors. Research on Parkinson’s disease genetics has been an effortful struggle, while new technologies and novel study designs served as indispensable boosters. Until now, 90 loci and 20 disease-causing gene mutations have been identified. In this study we describe a novel non-parametric approach to GWAS meta-analysis and its application in PD genetics.

Methods

A literature search was conducted to identify Genome-Wide Association Studies (GWAS) regarding Parkinson’s disease. We applied predefined inclusion criteria and extracted the reported SNPs and their respective position and statistical significance. We divided all chromosomes in approximately equal genetic distance segments called bins and recorded the most significant SNP from each bin and each study and ranked them in terms of their p-value. Ranks from each bin were summed, averaged and added in a heterogeneity-based analysis using the METRADISC-XL software. Weighted and unweighted analysis was performed.

Results

Five-hundred and forty-three SNPs and their respective p-values from 15 studies were matched in their corresponding bins. The METRADISC-XL analysis resulted in 7 bins with a significant p-value. A bin on chromosome 4 where the SNCA gene is located found with genome-wide significant association with Parkinson’s Disease.

Conclusion

This is the first time a non-parametric method is applied in GWAS meta-analysis. The results add some insight on the overall understanding of Parkinson’s disease genetics and serve as a first step of further convergent analysis with Genome-wide linkage studies.

Potential Common Genetic Risks of Sporadic Parkinson's Disease and Amyotrophic Lateral Sclerosis in the Han Population of Mainland China

Sporadic Parkinson’s disease (sPD) and sporadic amyotrophic lateral sclerosis (sALS) are neurodegenerative diseases characterized by progressive and selective neuron death, with some genetic similarities. In order to investigate the genetic risk factors common to both sPD and sALS, we carried out a screen of risk alleles for sALS and related loci in 530 sPD patients and 530 controls from the Han population of Mainland China (HPMC). We selected 27 single-nucleotide polymorphisms in 10 candidate genes associated with sALS, and we performed allelotyping and genotyping to determine their frequencies in the study population as well as bioinformatics analysis to assess their functional significance in these diseases. The minor alleles of rs17115303 in DAB adaptor protein 1 (DAB1) gene and rs6030462 in protein tyrosine phosphatase receptor type T (PTPRT) gene were correlated with increased risk of both sPD and sALS. Polymorphisms of rs17115303 and rs6030462 were associated with alterations in transcription factor binding sites, secondary structures, long non-coding RNA interactions, and nervous system regulatory networks; these changes involved biological processes associated with neural cell development, differentiation, neurogenesis, migration, axonogenesis, cell adhesion, and metabolism of phosphate-containing compounds. Thus, variants of DAB1 gene (rs17115303) and PTPRT gene (rs6030462) are risk factors common to sPD and sALS in the HPMC. These findings provide insight into the molecular pathogenesis of both diseases and can serve as a basis for the development of targeted therapies.

Understanding the Potential of Genome Editing in Parkinson's Disease

CRISPR is a simple and cost-efficient gene-editing technique that has become increasingly popular over the last decades. Various CRISPR/Cas-based applications have been developed to introduce changes in the genome and alter gene expression in diverse systems and tissues. These novel gene-editing techniques are particularly promising for investigating and treating neurodegenerative diseases, including Parkinson's disease, for which we currently lack efficient disease-modifying treatment options. Gene therapy could thus provide treatment alternatives, revolutionizing our ability to treat this disease. Here, we review our current knowledge on the genetic basis of Parkinson's disease to highlight the main biological pathways that become disrupted in Parkinson's disease and their potential as gene therapy targets. Next, we perform a comprehensive review of novel delivery vehicles available for gene-editing applications, critical for their successful application in both innovative research and potential therapies. Finally, we review the latest developments in CRISPR-based applications and gene therapies to understand and treat Parkinson's disease. We carefully examine their advantages and shortcomings for diverse gene-editing applications in the brain, highlighting promising avenues for future research.

Novel EDGE encoding method enhances ability to identify genetic interactions

Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to demonstrate identical genetic models. However, running SNP-SNP interaction analyses with every combination of encodings raises the multiple testing burden. Here, we present a novel and flexible encoding for genetic interactions, the elastic data-driven genetic encoding (EDGE), in which SNPs are assigned a heterozygous value based on the genetic model they demonstrate in a dataset prior to interaction testing. We assessed the power of EDGE to detect genetic interactions using 29 combinations of simulated genetic models and found it outperformed the traditional encoding methods across 10%, 30%, and 50% minor allele frequencies (MAFs). Further, EDGE maintained a low false-positive rate, while additive and dominant encodings demonstrated inflation. We evaluated EDGE and the traditional encodings with genetic data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes: age-related macular degeneration (AMD), age-related cataract, glaucoma, type 2 diabetes (T2D), and resistant hypertension. A multi-encoding genome-wide association study (GWAS) for each phenotype was performed using the traditional encodings, and the top results of the multi-encoding GWAS were considered for SNP-SNP interaction using the traditional encodings and EDGE. EDGE identified a novel SNP-SNP interaction for age-related cataract that no other method identified: rs7787286 (MAF: 0.041; intergenic region of chromosome 7)–rs4695885 (MAF: 0.34; intergenic region of chromosome 4) with a Bonferroni LRT p of 0.018. A SNP-SNP interaction was found in data from the UK Biobank within 25 kb of these SNPs using the recessive encoding: rs60374751 (MAF: 0.030) and rs6843594 (MAF: 0.34) (Bonferroni LRT p: 0.026). We recommend using EDGE to flexibly detect interactions between SNPs exhibiting diverse action.

Although traditional genetic encodings are widely implemented in genetics research, including in genome-wide association studies (GWAS) and epistasis, each method makes assumptions that may not reflect the underlying etiology. Here, we introduce a novel encoding method that estimates and assigns an individualized data-driven encoding for each single nucleotide polymorphism (SNP): the elastic data-driven genetic encoding (EDGE). With simulations, we demonstrate that this novel method is more accurate and robust than traditional encoding methods in estimating heterozygous genotype values, reducing the type I error, and detecting SNP-SNP interactions. We further applied the traditional encodings and EDGE to biomedical data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes, and EDGE identified a novel interaction for age-related cataract not detected by traditional methods, which replicated in data from the UK Biobank. EDGE provides an alternative approach to understanding and modeling diverse SNP models and is recommended for studying complex genetics in common human phenotypes.

Understanding the genetics of systemic lupus erythematosus using Bayesian statistics and gene network analysis

The publication of genetic epidemiology meta-analyses has increased rapidly, but it has been suggested that many of the statistically significant results are false positive. In addition, most such meta-analyses have been redundant, duplicate, and erroneous, leading to research waste. In addition, since most claimed candidate gene associations were false-positives, correctly interpreting the published results is important. In this review, we emphasize the importance of interpreting the results of genetic epidemiology meta-analyses using Bayesian statistics and gene network analysis, which could be applied in other diseases.

Common Factors in Neurodegeneration: A Meta-Study Revealing Shared Patterns on a Multi-Omics Scale

Neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS) are heterogeneous, progressive diseases with frequently overlapping symptoms characterized by a loss of neurons. Studies have suggested relations between neurodegenerative diseases for many years (e.g., regarding the aggregation of toxic proteins or triggering endogenous cell death pathways). We gathered publicly available genomic, transcriptomic, and proteomic data from 177 studies and more than one million patients to detect shared genetic patterns between the neurodegenerative diseases on three analyzed omics-layers. The results show a remarkably high number of shared differentially expressed genes between the transcriptomic and proteomic levels for all conditions, while showing a significant relation between genomic and proteomic data between AD and PD and AD and ALS. We identified a set of 139 genes being differentially expressed in several transcriptomic experiments of all four diseases. These 139 genes showed overrepresented gene ontology (GO) Terms involved in the development of neurodegeneration, such as response to heat and hypoxia, positive regulation of cytokines and angiogenesis, and RNA catabolic process. Furthermore, the four analyzed neurodegenerative diseases (NDDs) were clustered by their mean direction of regulation throughout all transcriptomic studies for this set of 139 genes, with the closest relation regarding this common gene set seen between AD and HD. GO-Term and pathway analysis of the proteomic overlap led to biological processes (BPs), related to protein folding and humoral immune response. Taken together, we could confirm the existence of many relations between Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis on transcriptomic and proteomic levels by analyzing the pathways and GO-Terms arising in these intersections. The significance of the connection and the striking relation of the results to processes leading to neurodegeneration between the transcriptomic and proteomic data for all four analyzed neurodegenerative diseases showed that exploring many studies simultaneously, including multiple omics-layers of different neurodegenerative diseases simultaneously, holds new relevant insights that do not emerge from analyzing these data separately. Furthermore, the results shed light on processes like the humoral immune response that have previously been described only for certain diseases. Our data therefore suggest human patients with neurodegenerative diseases should be addressed as complex biological systems by integrating multiple underlying data sources.

Identifying potential gene biomarkers for Parkinson's disease through an information entropy based approach

Parkinson's disease (PD) is a chronic, progressive neurodegenerative disease and represents the most common disease of this type, after Alzheimer's dementia. It is characterized by motor and nonmotor features and by a long prodromal stage that lasts many years. Genetic research has shown that PD is a complex and multisystem disorder. To capture the molecular complexity of this disease we used a complex network approach. We maximized the information entropy of the gene co-expression matrix betweenness to obtain a gene adjacency matrix; then we used a fast greedy algorithm to detect communities. Finally we applied principal component analysis on the detected gene communities, with the ultimate purpose of discriminating between PD patients and healthy controls by means of a random forests classifier. We used a publicly available substantia nigra microarray dataset, GSE20163, from NCBI GEO database, containing gene expression profiles for 10 PD patients and 18 normal controls. With this methodology we identified two gene communities that discriminated between the two groups with mean accuracy of 0.88 ± 0.03 and 0.84 ± 0.03, respectively, and validated our results on an independent microarray experiment. The two gene communities presented a considerable reduction in size, over 100 times, compared to the initial network and were stable within a range of tested parameters. Further research focusing on the restricted number of genes belonging to the selected communities may reveal essential mechanisms responsible for PD at a network level and could contribute to the discovery of new biomarkers for PD.

Network Protein Interaction in Parkinson's Disease and Periodontitis Interplay: A Preliminary Bioinformatic Analysis

Recent studies supported a clinical association between Parkinson's disease (PD) and periodontitis. Hence, investigating possible interactions between proteins associated to these two conditions is of interest. In this study, we conducted a protein-protein network interaction analysis with recognized genes encoding proteins with variants strongly associated with PD and periodontitis. Genes of interest were collected via the Genome-Wide Association Studies (GWAS) database. Then, we conducted a protein interaction analysis, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, with a highest confidence cutoff of 0.9 and sensitivity analysis with confidence cutoff of 0.7. Our protein network casts a comprehensive analysis of potential protein-protein interactions between PD and periodontitis. This analysis may underpin valuable information for new candidate molecular mechanisms between PD and periodontitis and may serve new potential targets for research purposes. These results should be carefully interpreted, giving the limitations of this approach.

Identification of Disease-Associated Variants by Targeted Gene Panel Resequencing in Parkinson's Disease

Background: Recent advanced technologies, such as high-throughput sequencing, have enabled the identification of a broad spectrum of variants. Using targeted-gene-panel resequencing for Parkinson's disease (PD)-associated genes, we have occasionally found several single-nucleotide variants (SNVs), which are thought to be disease-associated, in PD patients. To confirm the significance of these potentially disease-associated variants, we performed genome association analyses, using next-generation target resequencing, to evaluate the associations between the identified SNVs and PD. Methods: We obtained genomic DNA from 766 patients, who were clinically diagnosed with PD, and 336 healthy controls, all of Japanese origin. All data were analyzed using Ion AmpliSeq panel sequences, with 29 PD- or dementia-associated genes in a single panel. We excluded any variants that did not comply with the Hardy-Weinberg equilibrium in the control group. Variant frequencies in the PD and control groups were compared using PLINK. The identified variants were confirmed to a frequency difference of P < 0.05, after applying the Benjamini-Hochberg procedure using Fisher's exact test. The pathogenicity and prevalence of each variant were estimated based on a public gene database. Results: We identified three rare variants that were significantly associated with PD: rs201012663/rs150500694 in SYNJ1 and rs372754391 in DJ-1, which are intronic variants, and rs7412 in ApoE, which is an exonic variant. The variants in SYNJ1 and ApoE were frequently identified in the control group, and rs201012663/rs150500694 in SYNJ1 may play a protective role against PD. The DJ-1 variant was frequently identified in the PD group, with a high odds ratio of 2.2. Conclusion: The detected variants may represent genetic modifiers or disease-related variants in PD. Targeted-gene-panel resequencing may represent a useful method for detecting disease-causing variants and genetic association studies in PD.

Multiplicity of α-Synuclein Aggregated Species and Their Possible Roles in Disease

α-Synuclein amyloid aggregation is a defining molecular feature of Parkinson's disease, Lewy body dementia, and multiple system atrophy, but can also be found in other neurodegenerative disorders such as Alzheimer's disease. The process of α-synuclein aggregation can be initiated through alternative nucleation mechanisms and dominated by different secondary processes giving rise to multiple amyloid polymorphs and intermediate species. Some aggregated species have more inherent abilities to induce cellular stress and toxicity, while others seem to be more potent in propagating neurodegeneration. The preference for particular types of polymorphs depends on the solution conditions and the cellular microenvironment that the protein encounters, which is likely related to the distinct cellular locations of α-synuclein inclusions in different synucleinopathies, and the existence of disease-specific amyloid polymorphs. In this review, we discuss our current understanding on the nature and structure of the various types of α-synuclein aggregated species and their possible roles in pathology. Precisely defining these distinct α-synuclein species will contribute to understanding the molecular origins of these disorders, developing accurate diagnoses, and designing effective therapeutic interventions for these highly debilitating neurodegenerative diseases.

Genome-wide identification of m6A-associated single-nucleotide polymorphisms in Parkinson's disease

N⁶-methyladenosine (m⁶A)-associated single nucleotide polymorphisms (SNPs) play a vital role in several neurological diseases. However, little is known about the relationship between m⁶A modification and Parkinson's disease (PD). We investigated potential functional variants of m⁶A-SNPs from large-scale genome-wide association studies (GWAS) in PD patients. The candidate m⁶A-SNPs were further assessed by expression quantitative trait loci (eQTL) analysis and differential gene expression analysis. We identified 12 m⁶A-SNPs that were significantly associated with PD risk. Further, eQTL and expression analyses identified five of these m⁶A-SNPs (rs75072999 of GAK, rs1378602, rs4924839 and rs8071834 of ALKBH5, and rs1033500 of C6orf10) that were associated with altered gene expression in PD. Our results suggest that m⁶A-SNPs could play a role in PD risk. Future studies are needed to confirm these PD-associated m⁶A-SNPs and elucidate their mechanisms.

SNPeffect: identifying functional roles of SNPs using metabolic networks

Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome-wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype-to-phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme-coding genes. To this end, we also constructed a genome-scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth-determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.

[Genetic testing for Parkinson's disease: indication and practical implementation]

More than 20 years have passed since the first description of a monogenic cause of Parkinson's disease. Despite the tremendous advances of genetic testing these techniques are rarely used in Parkinson's disease. However, genetic tests in patients with Parkinson's syndrome will play an important role in the future. This is not only to ensure the diagnosis of Parkinson's patients with a young onset and / or a positive family history, but also in the context of personalised medicine with new therapeutic options. In the following we would like to give an overview of the basics of genetic testing, the legal requirements, the procedure for genetic testing and an outlook into the future for hereditary Parkinson's diseases.

FUS-mediated dysregulation of Sema5a, an autism-related gene, in FUS mice with hippocampus-dependent cognitive deficits

Pathological fused in sarcoma (FUS) inclusions are found in 10% of patients with frontotemporal dementia and those with amyotrophic lateral sclerosis (ALS) carrying FUS mutations. Current work indicates that FUS mutations may incur gain-of-toxic functions to drive ALS pathogenesis. However, how FUS dysfunction may affect cognition remains elusive. Using a mouse model expressing wild-type human FUS mimicking the endogenous expression pattern and level within the central nervous system, we found that they developed hippocampus-mediated cognitive deficits accompanied by an age-dependent reduction in spine density and long-term potentiation in their hippocampus. However, there were no apparent FUS aggregates, nuclear envelope defects and cytosolic FUS accumulation. These suggest that these proposed pathogenic mechanisms may not be the underlying causes for the observed cognitive deficits. Unbiased transcriptomic analysis identified expression changes in a small set of genes with preferential expression in the neurons and oligodendrocyte lineage cells. Of these, we focused on Sema5a, a gene involved in axon guidance, spine dynamics, Parkinson's disease and autism spectrum disorders. Critically, FUS binds directly to Sema5a mRNA and regulates Sema5a expression in a FUS-dose-dependent manner. Taken together, our data suggest that FUS-driven Sema5a deregulation may underlie the cognitive deficits in FUS transgenic mice.

Genetic predispositions of Parkinson's disease revealed in patient-derived brain cells

Parkinson's disease (PD) is the second most prevalent neurological disorder and has been the focus of intense investigations to understand its etiology and progression, but it still lacks a cure. Modeling diseases of the central nervous system in vitro with human induced pluripotent stem cells (hiPSC) is still in its infancy but has the potential to expedite the discovery and validation of new treatments. Here, we discuss the interplay between genetic predispositions and midbrain neuronal impairments in people living with PD. We first summarize the prevalence of causal Parkinson's genes and risk factors reported in 74 epidemiological and genomic studies. We then present a meta-analysis of 385 hiPSC-derived neuronal lines from 67 recent independent original research articles, which point towards specific impairments in neurons from Parkinson's patients, within the context of genetic predispositions. Despite the heterogeneous nature of the disease, current iPSC models reveal converging molecular pathways underlying neurodegeneration in a range of familial and sporadic forms of Parkinson's disease. Altogether, consolidating our understanding of robust cellular phenotypes across genetic cohorts of Parkinson's patients may guide future personalized drug screens in preclinical research.

Omics in Neurodegenerative Disease: Hope or Hype?

The past 15 years have seen a boom in the use and integration of 'omic' approaches (limited here to genomic, transcriptomic, and epigenomic techniques) to study neurodegenerative disease in an unprecedented way. We first highlight advances in and the limitations of using such approaches in the neurodegenerative disease literature, with a focus on Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal lobar degeneration (FTLD), and amyotrophic lateral sclerosis (ALS). We next discuss how these studies can advance human health in the form of generating leads for downstream mechanistic investigation or yielding polygenic risk scores (PRSs) for prognostication. However, we argue that these approaches constitute a new form of molecular description, analogous to clinical or pathological description, that alone does not hold the key to solving these complex diseases.

WITHDRAWN: Genetics of Parkinson's disease

The Publisher regrets that this article is an accidental duplication of an article that has already been published, DOI of original article: https://doi.org/10.1016/j.mcp.2016.11.001. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.

Functions of Plexins/Neuropilins and Their Ligands during Hippocampal Development and Neurodegeneration

There is emerging evidence that molecules, receptors, and signaling mechanisms involved in vascular development also play crucial roles during the development of the nervous system. Among others, specific semaphorins and their receptors (neuropilins and plexins) have, in recent years, attracted the attention of researchers due to their pleiotropy of functions. Their functions, mainly associated with control of the cellular cytoskeleton, include control of cell migration, cell morphology, and synapse remodeling. Here, we will focus on their roles in the hippocampal formation that plays a crucial role in memory and learning as it is a prime target during neurodegeneration.

[Genetics of movement disorders-rare but important]

Rare genetic movement disorders are a heterogeneous group of diseases. The causes of many of these rare movement disorders could be resolved due to the progress in molecular genetic diagnostics. This led to a better pathophysiological characterization of rare movement disorders and also to the fact that many phenotypical overlaps could be found between different diseases. The classification of genetic results requires a close cooperation between neurologists and geneticists. Therefore, modern diagnostic procedures cannot replace the clinical classification of genetic movement disorders and the exact patient history. This article provides the reader with an overview of the most important groups of genetic movement disorders. Genetic Parkinson syndromes, dystonia, essential tremor, genetic chorea, cerebellar ataxia and hereditary spastic paraplegia are dealt with in detail. For a better understanding individual genetic terms are explained and differences in molecular genetic diagnostics are presented.

The association between CD157/BST1 polymorphisms and the susceptibility of Parkinson's disease: a meta-analysis

Background: Different studies have provided some evidence for the association between BST1 polymorphisms and Parkinson's disease (PD). The extent to which these genetic effects are consistent across different populations is unknown. Methods: A meta-analysis of PD case-control studies using a common set of three variants was conducted. Published reports were obtained from electronic databases including Pubmed, Embase, Chinese National Knowledge Infrastructure (CNKI) and Cochrane Library databases between August 2010 and January 2018. Results: A total of 11 individual studies with 8,725 cases and 17,079 controls were included. The results showed statistically significant association between the dominant model of rs11931532 and PD risk in Asian populations (P=0.006, OR [95% CI]=1.22 [1.06-1.41]). Significant association was also detected between the allelic, dominant, and recessive models of rs4698412 and PD risk in Asian populations (allelic model: P<0.00001, OR [95% CI]=1.22 [1.16-1.29]; dominant model: P<0.00001, OR [95%CI]=1.35 [1.20-1.52]; recessive model; P=0.0003, OR [95% CI]=1.30 [1.13-1.50]). Nevertheless, the pooled analyses suggested that no significant association was uncovered between rs11724635 and PD risk (P>0.05). Conclusion: The meta-analysis suggests that the rs11931532 and rs4698412, but not rs11724635 might be risk factors for PD in Asian populations.

GLIS1-3 transcription factors: critical roles in the regulation of multiple physiological processes and diseases

Krüppel-like zinc finger proteins form one of the largest families of transcription factors. They function as key regulators of embryonic development and a wide range of other physiological processes, and are implicated in a variety of pathologies. GLI-similar 1-3 (GLIS1-3) constitute a subfamily of Krüppel-like zinc finger proteins that act either as activators or repressors of gene transcription. GLIS3 plays a critical role in the regulation of multiple biological processes and is a key regulator of pancreatic β cell generation and maturation, insulin gene expression, thyroid hormone biosynthesis, spermatogenesis, and the maintenance of normal kidney functions. Loss of GLIS3 function in humans and mice leads to the development of several pathologies, including neonatal diabetes and congenital hypothyroidism, polycystic kidney disease, and infertility. Single nucleotide polymorphisms in GLIS3 genes have been associated with increased risk of several diseases, including type 1 and type 2 diabetes, glaucoma, and neurological disorders. GLIS2 plays a critical role in the kidney and GLIS2 dysfunction leads to nephronophthisis, an end-stage, cystic renal disease. In addition, GLIS1-3 have regulatory functions in several stem/progenitor cell populations. GLIS1 and GLIS3 greatly enhance reprogramming efficiency of somatic cells into induced embryonic stem cells, while GLIS2 inhibits reprogramming. Recent studies have obtained substantial mechanistic insights into several physiological processes regulated by GLIS2 and GLIS3, while a little is still known about the physiological functions of GLIS1. The localization of some GLIS proteins to the primary cilium suggests that their activity may be regulated by a downstream primary cilium-associated signaling pathway. Insights into the upstream GLIS signaling pathway may provide opportunities for the development of new therapeutic strategies for diabetes, hypothyroidism, and other diseases.

No evidence for DNM3 as genetic modifier of age at onset in idiopathic Parkinson's disease

Parkinson's disease (PD) is a disorder with highly variable clinical phenotype. The identification of genetic variants modifying age at onset and other traits is of great interest because it may provide insight into disease mechanisms and potential therapeutic targets. A variant in the DNM3 gene (rs2421947) has been reported as a genetic modifier of age at onset in LRRK2-associated PD. To test the possible effect of genetic variation in DNM3 on age at onset in idiopathic PD, we examined rs2421947 in a total of 5918 patients with PD from seven data sets. We also assessed the potential effect of all common variants in the DNM3 locus. There was no significant association between rs2421947 and age at onset in any of the individual studies. Meta-analysis of the seven studies was nonsignificant and the between-study heterogeneity was minimal. No other common variants within the DNM3 locus affected age at onset. In conclusion, we find no evidence of an association between DNM3 variants and age at onset in idiopathic PD.

Advances in the discovery of genetic risk factors for complex forms of neurodegenerative disorders: contemporary approaches, success, challenges and prospects

Neurodegenerative diseases constitute a large proportion of disorders in elderly, majority being sporadic in occurrence with ∼5-10% familial. A strong genetic component underlies the Mendelian forms but nongenetic factors together with genetic vulnerability contributes to the complex sporadic forms. Several gene discoveries in the familial forms have provided novel insights into the pathogenesis of neurodegeneration with implications for treatment. Conversely, findings from genetic dissection of the sporadic forms, despite large genomewide association studies and more recently whole exome and whole genome sequencing, have been limited. This review provides a concise account of the genetics that we know, the pathways that they implicate, the challenges that are faced and the prospects that are envisaged for the sporadic, complex forms of neurodegenerative diseases, taking four most common conditions, namely Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis and Huntington disease as examples. Poor replication across studies, inability to establish genotype-phenotype correlations and the overall failure to predict risk and/or prevent disease in this group poses a continuing challenge. Among others, clinical heterogeneity emerges as the most important impediment warranting newer approaches. Advanced computational and system biology tools to analyse the big data are being generated and the alternate strategy such as subgrouping of case-control cohorts based on deep phenotyping using the principles of Ayurveda to overcome current limitation of phenotype heterogeneity seem to hold promise. However, at this point, with advances in discovery genomics and functional analysis of putative determinants with translation potential for the complex forms being minimal, stem cell therapies are being attempted as potential interventions. In this context, the possibility to generate patient derived induced pluripotent stem cells, mutant/gene/genome correction through CRISPR/Cas9 technology and repopulating the specific brain regions with corrected neurons, which may fulfil the dream of personalized medicine have been mentioned briefly. Understanding disease pathways/biology using this technology, with implications for development of novel therapeutics are optimistic expectations in the near future.

Genomic convergence of locus-based GWAS meta-analysis identifies AXIN1 as a novel Parkinson's gene

Parkinson's disease (PD) is a common, disabling neurodegenerative disorder with significant genetic underpinnings. Multiple genome-wide association studies (GWAS) have been conducted with identification of several PD loci. However, these only explain about 25% of PD genetic risk indicating that additional loci of modest effect remain to be discovered. Association clustering methods such as gene-based tests are more powerful than single-variant analysis for identifying modest genetic effects. Combined with the locus-based algorithm, OASIS, the most significant association signals can be homed in. Here, two dbGAP GWAS datasets (7415 subjects (2750 PD and 4845 controls) genotyped for 0.78 million SNPs) were analyzed using combined clustering algorithms to identify 88 PD candidate genes in 24 loci. These were further investigated for gene expression in substantia nigra (SN) of PD and control subjects on GEO datasets. Expression differences were also assessed in normal brains SN versus white matter on BRAINEAC datasets. This genetic and functional analysis identified AXIN1, a key regulator of Wnt/β-catenin signaling, as a novel PD gene. This finding links PD with inflammation. Other significantly associated genes were CSMD1, CLDN1, ZNF141, ZNF721, RHOT2, RICTOR, KANSL1, and ARHGAP27. Novel PD genes were identified using genomic convergence of gene-wide and locus-based tests and expression studies on archived datasets.

OX40 Gene and Serum Protein Expression Profiles in Patients with Parkinson's Disease

Objective:

Inflammation of the immune system and the central nervous system has been known as an important predisposing factor for Parkinson’s disease (PD). Increased expression of OX40 protein (CD134) is a known factor for increased inflammation and initiation of NF-kappa-B signaling pathway in different diseases. We aimed to investigate the expression of OX40 at the transcript and serum protein levels.

Materials and Methods:

Twenty individuals with PD and 20 healthy individuals, as controls, were enrolled in this casecontrol study. Expression of OX40 at the transcript level and serum protein levels were measured by quantitative real-time polymerase chain reaction (qRT-PCR) and enzyme-linked immunosorbent assays respectively.

Results:

The mean expression level of OX40 was increased in patients but not at a significant level (P>0.05). Consistently, the mean serum concentration of OX40 showed a mild, but non-significant, increase in the patients (P>0.05).

Conclusion:

We conclude that OX40 expression at either the transcript or protein level has no diagnostic utility in asymptomatic PD. This shows the need for clinical, cellular and interventional research to detect new robust biomarkers.

Deregulation of autophagy and vesicle trafficking in Parkinson's disease

Parkinson's disease (PD) is a common neurodegenerative disease characterized pathologically by the selective loss of dopaminergic neurons in the substantia nigra and the intracellular accumulation of α-synuclein in the Lewy bodies. While the pathogenic mechanisms of PD are poorly understood, many lines of evidence point to a role of altered autophagy and membrane trafficking in the development of the disease. Emerging studies show that connections between the deregulation of autophagy and synaptic vesicle (SV) trafficking may contribute to PD. Here we review the evidence that many PD related-genes have roles in both autophagy and SV trafficking and examine how deregulation of these pathways contributes to PD pathogenesis. This review also discusses recent studies aimed at uncovering the role of PD-linked genes in autophagy-lysosome function.

The Length of SNCA Rep1 Microsatellite May Influence Cognitive Evolution in Parkinson's Disease

Background

Alpha-synuclein is a constituent of Lewy bodies and mutations of its gene cause familial Parkinson's disease (PD). A previous study showed that a variant of the alpha-synuclein gene (SNCA), namely the 263 bp allele of Rep1 was associated with faster motor progression in PD. On the contrary, a recent report failed to detect a detrimental effect of Rep1 263 on both motor and cognitive outcomes in PD. Aim of this study was to evaluate the influence of the Rep1 variants on disease progression in PD patients.

Methods

We recruited and genotyped for SNCA Rep1 426 PD patients with age at onset ≥40 years and disease duration ≥4 years. We then analyzed frequency and time of occurrence of wearing-off, dyskinesia, freezing of gait, visual hallucinations, and dementia using a multivariate Cox's proportional hazards regression model.

Results

SNCA Rep1 263 carriers showed significantly increased risk of both dementia (HR = 3.03) and visual hallucinations (HR = 2.69) compared to 263 non-carriers. Risk of motor complications did not differ in the two groups.

Conclusion

SNCA Rep1 263 allele is associated with a worse cognitive outcome in PD.

Semaphorins and their Signaling Mechanisms

Semaphorins are extracellular signaling proteins that are essential for the development and maintenance of many organs and tissues. The more than 20-member semaphorin protein family includes secreted, transmembrane and cell surface-attached proteins with diverse structures, each characterized by a single cysteine-rich extracellular sema domain, the defining feature of the family. Early studies revealed that semaphorins function as axon guidance molecules, but it is now understood that semaphorins are key regulators of morphology and motility in many different cell types including those that make up the nervous, cardiovascular, immune, endocrine, hepatic, renal, reproductive, respiratory and musculoskeletal systems, as well as in cancer cells. Semaphorin signaling occurs predominantly through Plexin receptors and results in changes to the cytoskeletal and adhesive machinery that regulate cellular morphology. While much remains to be learned about the mechanisms underlying the effects of semaphorins, exciting work has begun to reveal how semaphorin signaling is fine-tuned through different receptor complexes and other mechanisms to achieve specific outcomes in various cellular contexts and physiological systems. These and future studies will lead to a more complete understanding of semaphorin-mediated development and to a greater understanding of how these proteins function in human disease.

Genome-Wide Interactions with Dairy Intake for Body Mass Index in Adults of European Descent

SCOPE

Body weight responds variably to the intake of dairy foods. Genetic variation may contribute to inter-individual variability in associations between body weight and dairy consumption.

METHODS AND RESULTS

A genome-wide interaction study to discover genetic variants that account for variation in BMI in the context of low-fat, high-fat and total dairy intake in cross-sectional analysis was conducted. Data from nine discovery studies (up to 25 513 European descent individuals) were meta-analyzed. Twenty-six genetic variants reached the selected significance threshold (p-interaction <10-7) , and six independent variants (LINC01512-rs7751666, PALM2/AKAP2-rs914359, ACTA2-rs1388, PPP1R12A-rs7961195, LINC00333-rs9635058, AC098847.1-rs1791355) were evaluated meta-analytically for replication of interaction in up to 17 675 individuals. Variant rs9635058 (128 kb 3' of LINC00333) was replicated (p-interaction = 0.004). In the discovery cohorts, rs9635058 interacted with dairy (p-interaction = 7.36 × 10-8) such that each serving of low-fat dairy was associated with 0.225 kg m-2 lower BMI per each additional copy of the effect allele (A). A second genetic variant (ACTA2-rs1388) approached interaction replication significance for low-fat dairy exposure.

CONCLUSION

Body weight responses to dairy intake may be modified by genotype, in that greater dairy intake may protect a genetic subgroup from higher body weight.

From Genomics to Omics Landscapes of Parkinson's Disease: Revealing the Molecular Mechanisms

Molecular mechanisms of Parkinson's disease (PD) have already been investigated in various different omics landscapes. We reviewed the literature about different omics approaches between November 2005 and November 2017 to depict the main pathological pathways for PD development. In total, 107 articles exploring different layers of omics data associated with PD were retrieved. The studies were grouped into 13 omics layers: genomics-DNA level, transcriptomics, epigenomics, proteomics, ncRNomics, interactomics, metabolomics, glycomics, lipidomics, phenomics, environmental omics, pharmacogenomics, and integromics. We discussed characteristics of studies from different landscapes, such as main findings, number of participants, sample type, methodology, and outcome. We also performed curation and preliminary synthesis of multiple omics data, and identified overlapping results, which could lead toward selection of biomarkers for further validation of PD risk loci. Biomarkers could support the development of targeted prognostic/diagnostic panels as a tool for early diagnosis and prediction of progression rate and prognosis. This review presents an example of a comprehensive approach to revealing the underlying processes and risk factors of a complex disease. It urges scientists to structure the already known data and integrate it into a meaningful context.

Alpha-Synuclein transgenic mice, h-α-SynL62, display α-Syn aggregation and a dopaminergic phenotype reminiscent of Parkinson's disease

Alpha-Synuclein (α-Syn) accumulation is considered a major risk factor for the development of synucleinopathies such as Parkinson's disease (PD) and dementia with Lewy bodies. We have generated mice overexpressing full-length human α-Syn fused to a membrane-targeting signal sequence under the control of the mouse Thy1-promotor. Three separate lines (L56, L58 and L62) with similar gene expression levels, but considerably heightened protein accumulation in L58 and L62, were established. In L62, there was widespread labelling of α-Syn immunoreactivity in brain including spinal cord, basal forebrain, cortex and striatum. Interestingly, there was no detectable α-Syn expression in dopaminergic neurones of the substantia nigra, but strong human α-Syn reactivity in glutamatergic synapses. The human α-Syn accumulated during aging and formed PK-resistant, thioflavin-binding aggregates. Mice displayed early onset bradykinesia and age progressive motor deficits. Functional alterations within the striatum were confirmed: L62 showed normal basal dopamine levels, but impaired dopamine release (upon amphetamine challenge) in the dorsal striatum measured by in vivo brain dialysis at 9 months of age. This impairment was coincident with a reduced response to amphetamine in the activity test. L62 further displayed greater sensitivity to low doses of the dopamine receptor 1 (D1) agonist SKF81297 but reacted normally to the D2 agonist quinpirole in the open field. Since accumulation of α-Syn aggregates in neurones and synapses and alterations in the dopaminergic tone are characteristics of PD, phenotypes reported for L62 present a good opportunity to further our understanding of motor dysfunction in PD and Lewy body dementia.

Increase of Reproductive Life Span Delays Age of Onset of Parkinson's Disease

One striking observation in Parkinson’s disease (PD) is the remarkable gender difference in incidence and prevalence of the disease. Data on gender differences with regard to disease onset, motor and non-motor symptoms, and dopaminergic medication are limited. Furthermore, whether estrogen status affects disease onset and progression of PD is controversially discussed. In this retrospective single center study, we extracted clinical data of 226 ambulatory PD patients and compared age of disease onset, disease stage, motor impairment, non-motor symptoms, and dopaminergic medication between genders. We applied a matched-pairs design to adjust for age and disease duration. To determine the effect of estrogen-related reproductive factors including number of children, age at menarche, and menopause on the age of onset, we applied a standardized questionnaire and performed a regression analysis. The male to female ratio in the present PD cohort was 1.9:1 (147 men vs. 79 women). Male patients showed increased motor impairment than female patients. The levodopa equivalent daily dose was increased by 18.9% in male patients compared to female patients. Matched-pairs analysis confirmed the increased dose of dopaminergic medication in male patients. No differences were observed in age of onset, type of medication, and non-motor symptoms between both groups. Female reproductive factors including number of children, age at menarche, and age at menopause were positively associated with a delay of disease onset up to 30 months. The disease-modifying role of estrogen-related outcome measures warrants further clinical and experimental studies targeting gender differences, specifically hormone-dependent pathways in PD.

Detecting genetic association through shortest paths in a bidirected graph

Genome-wide association studies (GWASs) commonly use marginal association tests for each single-nucleotide polymorphism (SNP). Because these tests treat SNPs as independent, their power will be suboptimal for detecting SNPs hidden by linkage disequilibrium (LD). One way to improve power is to use a multiple regression model. However, the large number of SNPs preclude simultaneous fitting with multiple regression, and subset regression is infeasible because of an exorbitant number of candidate subsets. We therefore propose a new method for detecting hidden SNPs having significant yet weak marginal association in a multiple regression model. Our method begins by constructing a bidirected graph locally around each SNP that demonstrates a moderately sized marginal association signal, the focal SNPs. Vertexes correspond to SNPs, and adjacency between vertexes is defined by an LD measure. Subsequently, the method collects from each graph all shortest paths to the focal SNP. Finally, for each shortest path the method fits a multiple regression model to all the SNPs lying in the path and tests the significance of the regression coefficient corresponding to the terminal SNP in the path. Simulation studies show that the proposed method can detect susceptibility SNPs hidden by LD that go undetected with marginal association testing or with existing multivariate methods. When applied to real GWAS data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), our method detected two groups of SNPs: one in a region containing the apolipoprotein E (APOE) gene, and another in a region close to the semaphorin 5A (SEMA5A) gene.

A novel relationship for schizophrenia, bipolar and major depressive disorder Part 5: a hint from chromosome 5 high density association screen

Familial clustering of schizophrenia (SCZ), bipolar disorder (BPD), and major depressive disorder (MDD) was systematically reported (Aukes, M. F. Genet Med 2012, 14, 338-341) and any two or even three of these disorders could co-exist in some families. In addition, evidence from symptomatology and psychopharmacology also imply that there are intrinsic connections between these three major disorders. A total of 56,569 single nucleotide polymorphism (SNPs) on chromosome 5 were genotyped by Affymetrix Genome-Wide Human SNP array 6.0 on 119 SCZ, 253 BPD (type-I), 177 MDD patients and 1000 controls. Associated SNPs and flanking genes was screen out systematically, and cadherin pathway genes (CDH6, CDH9, CDH10, CDH12, and CDH18) belong to outstanding genes. Unexpectedly, nearly all flanking genes of the associated SNPs distinctive for BPD and MDD were replicated in an enlarged cohort of 986 SCZ patients (P ≤ 9.9E-8). Considering multiple bits of evidence, our chromosome 5 analyses implicated that bipolar and major depressive disorder might be subtypes of schizophrenia rather than two independent disease entities. Also, cadherin pathway genes play important roles in the pathogenesis of the three major mental disorders.

Integrated molecular landscape of Parkinson's disease

Parkinson’s disease is caused by a complex interplay of genetic and environmental factors. Although a number of independent molecular pathways and processes have been associated with familial Parkinson’s disease, a common mechanism underlying especially sporadic Parkinson’s disease is still largely unknown. In order to gain further insight into the etiology of Parkinson’s disease, we here conducted genetic network and literature analyses to integrate the top-ranked findings from thirteen published genome-wide association studies of Parkinson’s disease (involving 13.094 cases and 47.148 controls) and other genes implicated in (familial) Parkinson’s disease, into a molecular interaction landscape. The molecular Parkinson’s disease landscape harbors four main biological processes—oxidative stress response, endosomal-lysosomal functioning, endoplasmic reticulum stress response, and immune response activation—that interact with each other and regulate dopaminergic neuron function and death, the pathological hallmark of Parkinson’s disease. Interestingly, lipids and lipoproteins are functionally involved in and influenced by all these processes, and affect dopaminergic neuron-specific signaling cascades. Furthermore, we validate the Parkinson’s disease -lipid relationship by genome-wide association studies data-based polygenic risk score analyses that indicate a shared genetic risk between lipid/lipoprotein traits and Parkinson’s disease. Taken together, our findings provide novel insights into the molecular pathways underlying the etiology of (sporadic) Parkinson’s disease and highlight a key role for lipids and lipoproteins in Parkinson’s disease pathogenesis, providing important clues for the development of disease-modifying treatments of Parkinson’s disease.

Lipids and lipoproteins play a central role in four key biological processes underlying Parkinson’s disease (PD). Using bioinformatics and other extensive analyses of previously published data, Geert Poelmans, Cornelius Klemann and colleagues in The Netherlands, Germany and the USA have mapped the interactions of proteins that are encoded by genes associated with both familial and sporadic forms of PD. They identify the oxidative stress response, lysosomal function, endoplasmic reticulum stress response and immune response activation as the main mechanisms leading to the death of dopaminergic neurons. Lipid signaling is implicated in all four of these processes and the authors find a link between the levels of particular lipids and lipoproteins and the risk of PD. These findings suggest that compounds that regulate lipid or lipoprotein levels offer a potential new treatment strategy for PD.

Frequency of GBA variants in autopsy-proven multiple system atrophy

BACKGROUND

Multiple system atrophy (MSA) is marked by abnormal inclusions of alpha-synuclein in oligodendrogliocytes. Etiology remains unknown. Variants in the glucocerebrosidase gene have been associated with other synucleinopathies, dementia with Lewy bodies and Parkinson disease. It is unclear whether glucocerebrosidase variants are associated with MSA.

OBJECTIVES

To analyze the frequency of glucocerebrosidase gene variants among autopsy-proven cases of MSA at a brain bank in New York City.

METHODS

The glucocerebrosidase gene was fully sequenced in the 17 autopsy-proven MSA cases with extractable DNA at the Columbia University New York Brain Bank from 2002 to 2016. To test if the MSA cases in the brain bank are enriched for GBA variants, we compared the GBA variant frequency in MSA to all brain bank cases with pure Alzheimer's disease (AD) at Columbia University for whom GBA genotype was available (n=82).

RESULTS

4/17 (23.5%) MSA cases carried glucocerebrosidase gene variants, including an individual homozygous for N370S, and one each who were heterozygous carriers of N370S, T369M and R496H. Among the comparator cases with pure AD, 3 of the 82 autopsies (3.7%) carried GBA variants (P = 0.0127, Fisher exact test), including one case each of N370S homozygote, and R496H and T369M heterozygous variant.

CONCLUSION

We found a higher frequency of glucocerebrosidase variants among pathologically diagnosed MSA cases in our brain bank compared to AD autopsies. This study demonstrates the need for further investigation into the role of glucocerebrosidase and lysosomal dysfunction in the etiology of MSA.

An updated review of Parkinson's disease genetics and clinicopathological correlations

Knowledge regarding the pathophysiological basis of Parkinson's disease (PD) has been greatly expanded over the past two decades, with extraordinary contributions from the field of genetics. However, genetic classifications became complex, difficult to follow, and at times misleading, by placing well-established monogenic forms of the disease along with others associated with risk loci, often ill characterized. The present paper summarizes the genetic, clinical, and neuropathological findings of the currently described monogenic forms of PD and also approaches the progress made in determining genetic risk factors for PD. Furthermore, the text incorporates the data into a recently proposed classification system that will hopefully bring a "user-friendly" approach to this issue. This paper also highlights a number of inconsistencies regarding classification of PD as a single, unique clinicopathological entity-in fact, in order to achieve the development of truly innovative therapies, PD should probably be regarded clinically as a "Parkinson's disease cluster", instead of a single disease. In the future, we hope that an in-depth and groundbreaking understanding of PD will allow the development of truly disease-modifying therapies that will target the molecular processes responsible for the cascade of pathological events underlying each form of PD.

The Birth of the Modern Era of Parkinson's Disease Genetics

Genetic understanding in Parkinson’s disease (PD) has followed a path of hard won evolution occasionally punctuated by revolution. While it was suggested early on by both Leroux and Gowers that heredity had a role to play in PD, this was a view that wasn’t widely enough held to even be unpopular. The dogma was that the disease was one of environmental provenance and while the evidence for this is still rather scarce, this view pervades in the minds of patients, clinicians, and scientists. Conversely the evidence linking genetics to PD is both overwhelming and growing. Here we describe the growth of genetics in PD from backwater to driving force, and the structure and shape of its future.

Structural basis for the dissociation of α-synuclein fibrils triggered by pressure perturbation of the hydrophobic core

Parkinson’s disease is a neurological disease in which aggregated forms of the α-synuclein (α-syn) protein are found. We used high hydrostatic pressure (HHP) coupled with NMR spectroscopy to study the dissociation of α-syn fibril into monomers and evaluate their structural and dynamic properties. Different dynamic properties in the non-amyloid-β component (NAC), which constitutes the Greek-key hydrophobic core, and in the acidic C-terminal region of the protein were identified by HHP NMR spectroscopy. In addition, solid-state NMR revealed subtle differences in the HHP-disturbed fibril core, providing clues to how these species contribute to seeding α-syn aggregation. These findings show how pressure can populate so far undetected α-syn species, and they lay out a roadmap for fibril dissociation via pathways not previously observed using other approaches. Pressure perturbs the cavity-prone hydrophobic core of the fibrils by pushing water inward, thereby inducing the dissociation into monomers. Our study offers the molecular details of how hydrophobic interaction and the formation of water-excluded cavities jointly contribute to the assembly and stabilization of the fibrils. Understanding the molecular forces behind the formation of pathogenic fibrils uncovered by pressure perturbation will aid in the development of new therapeutics against Parkinson’s disease.