Genome-wide genotyping in Parkinson's disease and neurologically normal controls: first stage analysis and public release of data

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.

Role of Signal-Transducing Adaptor Protein-1 for T Cell Activation and Pathogenesis of Autoimmune Demyelination and Airway Inflammation

Signal-transducing adaptor protein (STAP)-1 is an adaptor protein that is widely expressed in T cells. In this article, we show that STAP-1 upregulates TCR-mediated T cell activation and T cell-mediated airway inflammation. Using STAP-1 knockout mice and STAP-1-overexpressing Jurkat cells, we found that STAP-1 enhanced TCR signaling, resulting in increased calcium mobilization, NFAT activity, and IL-2 production. Upon TCR engagement, STAP-1 binding to ITK promoted formation of ITK-LCK and ITK-phospholipase Cγ1 complexes to induce downstream signaling. Consistent with the results, STAP-1 deficiency reduced the severity of symptoms in experimental autoimmune encephalomyelitis. Single-cell RNA-sequencing analysis revealed that STAP-1 is essential for accumulation of T cells and Ifng and Il17 expression in spinal cords after experimental autoimmune encephalomyelitis induction. Th1 and Th17 development was also attenuated in STAP-1 knockout naive T cells. Taken together, STAP-1 enhances TCR signaling and plays a role in T cell-mediated immune disorders.

Neuropathogenesis-on-chips for neurodegenerative diseases

Developing diagnostics and treatments for neurodegenerative diseases (NDs) is challenging due to multifactorial pathogenesis that progresses gradually. Advanced in vitro systems that recapitulate patient-like pathophysiology are emerging as alternatives to conventional animal-based models. In this review, we explore the interconnected pathogenic features of different types of ND, discuss the general strategy to modelling NDs using a microfluidic chip, and introduce the organoid-on-a-chip as the next advanced relevant model. Lastly, we overview how these models are being applied in academic and industrial drug development. The integration of microfluidic chips, stem cells, and biotechnological devices promises to provide valuable insights for biomedical research and developing diagnostic and therapeutic solutions for NDs.

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.

A multi-threaded approach to genotype pattern mining for detecting digenic disease genes

To locate disease-causing DNA variants on the human gene map, the customary approach has been to carry out a genome-wide association study for one variant after another by testing for genotype frequency differences between individuals affected and unaffected with disease. So-called digenic traits are due to the combined effects of two variants, often on different chromosomes, while individual variants may have little or no effect on disease. Machine learning approaches have been developed to find variant pairs underlying digenic traits. However, many of these methods have large memory requirements so that only small datasets can be analyzed. The increasing availability of desktop computers with large numbers of processors and suitable programming to distribute the workload evenly over all processors in a machine make a new and relatively straightforward approach possible, that is, to evaluate all existing variant and genotype pairs for disease association. We present a prototype of such a method with two components, Vpairs and Gpairs, and demonstrate its advantages over existing implementations of such well-known algorithms as Apriori and FP-growth. We apply these methods to published case-control datasets on age-related macular degeneration and Parkinson disease and construct an ROC curve for a large set of genotype patterns.

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.

C/EBPβ Regulates TFAM Expression, Mitochondrial Function and Autophagy in Cellular Models of Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder that results from the degeneration of dopaminergic neurons in the substantia nigra pars compacta (SNpc). Since there are only symptomatic treatments available, new cellular and molecular targets involved in the onset and progression of this disease are needed to develop effective treatments. CCAAT/Enhancer Binding Protein β (C/EBPβ) transcription factor levels are altered in patients with a variety of neurodegenerative diseases, suggesting that it may be a good therapeutic target for the treatment of PD. A list of genes involved in PD that can be regulated by C/EBPβ was generated by the combination of genetic and in silico data, the mitochondrial transcription factor A (TFAM) being among them. In this paper, we observed that C/EBPβ overexpression increased TFAM promoter activity. However, downregulation of C/EBPβ in different PD/neuroinflammation cellular models produced an increase in TFAM levels, together with other mitochondrial markers. This led us to propose an accumulation of non-functional mitochondria possibly due to the alteration of their autophagic degradation in the absence of C/EBPβ. Then, we concluded that C/EBPβ is not only involved in harmful processes occurring in PD, such as inflammation, but is also implicated in mitochondrial function and autophagy in PD-like conditions.

Regulation of mitophagy by the NSL complex underlies genetic risk for Parkinson's disease at 16q11.2 and MAPT H1 loci

Parkinson's disease is a common incurable neurodegenerative disease. The identification of genetic variants via genome-wide association studies has considerably advanced our understanding of the Parkinson's disease genetic risk. Understanding the functional significance of the risk loci is now a critical step towards translating these genetic advances into an enhanced biological understanding of the disease. Impaired mitophagy is a key causative pathway in familial Parkinson's disease, but its relevance to idiopathic Parkinson's disease is unclear. We used a mitophagy screening assay to evaluate the functional significance of risk genes identified through genome-wide association studies. We identified two new regulators of PINK1-dependent mitophagy initiation, KAT8 and KANSL1, previously shown to modulate lysine acetylation. These findings suggest PINK1-mitophagy is a contributing factor to idiopathic Parkinson's disease. KANSL1 is located on chromosome 17q21 where the risk associated gene has long been considered to be MAPT. While our data do not exclude a possible association between the MAPT gene and Parkinson's disease, they provide strong evidence that KANSL1 plays a crucial role in the disease. Finally, these results enrich our understanding of physiological events regulating mitophagy and establish a novel pathway for drug targeting in neurodegeneration.

Autophagopathies: from autophagy gene polymorphisms to precision medicine for human diseases

At a time when complex diseases affect globally 280 million people and claim 14 million lives every year, there is an urgent need to rapidly increase our knowledge into their underlying etiologies. Though critical in identifying the people at risk, the causal environmental factors (microbiome and/or pollutants) and the affected pathophysiological mechanisms are not well understood. Herein, we consider the variations of autophagy-related (ATG) genes at the heart of mechanisms of increased susceptibility to environmental stress. A comprehensive autophagy genomic resource is presented with 263 single nucleotide polymorphisms (SNPs) for 69 autophagy-related genes associated with 117 autoimmune, inflammatory, infectious, cardiovascular, neurological, respiratory, and endocrine diseases. We thus propose the term 'autophagopathies' to group together a class of complex human diseases the etiology of which lies in a genetic defect of the autophagy machinery, whether directly related or not to an abnormal flux in autophagy, LC3-associated phagocytosis, or any associated trafficking. The future of precision medicine for common diseases will lie in our ability to exploit these ATG SNP x environment relationships to develop new polygenetic risk scores, new management guidelines, and optimal therapies for afflicted patients.Abbreviations: ATG, autophagy-related; ALS-FTD, amyotrophic lateral sclerosis-frontotemporal dementia; ccRCC, clear cell renal cell carcinoma; CD, Crohn disease; COPD, chronic obstructive pulmonary disease; eQTL, expression quantitative trait loci; HCC, hepatocellular carcinoma; HNSCC, head and neck squamous cell carcinoma; GTEx, genotype-tissue expression; GWAS, genome-wide association studies; LAP, LC3-associated phagocytosis; LC3-II, phosphatidylethanolamine conjugated form of LC3; LD, linkage disequilibrium; LUAD, lung adenocarcinoma; MAF, minor allele frequency; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; NSCLC, non-small cell lung cancer; OS, overall survival; PtdIns3K CIII, class III phosphatidylinositol 3 kinase; PtdIns3P, phosphatidylinositol-3-phosphate; SLE, systemic lupus erythematosus; SNPs, single-nucleotide polymorphisms; mQTL, methylation quantitative trait loci; ULK, unc-51 like autophagy activating kinase; UTRs, untranslated regions; WHO, World Health Organization.

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.

Mitochondrial and autophagy-lysosomal pathway polygenic risk scores predict Parkinson's disease

Polygenic Risk Scores (PRS), which allow assessing an individuals' genetic risk for a complex disease, are calculated as the weighted number of genetic risk alleles in an individual's genome, with the risk alleles and their weights typically derived from the results of genome-wide association studies (GWAS). Among a wide range of applications, PRS can be used to identify at-risk individuals and select them for further clinical follow-up. Pathway PRS are genetic scores based on single nucleotide polymorphisms (SNPs) assigned to genes involved in major disease pathways. The aim of this study is to assess the predictive utility of PRS models constructed based on SNPs corresponding to two cardinal pathways in Parkinson's disease (PD) including mitochondrial PRS (Mito PRS) and autophagy-lysosomal PRS (ALP PRS). PRS models were constructed using the clumping-and-thresholding method in a German population as prediction dataset that included 371 cases and 249 controls, using SNPs discovered by the most recent PD-GWAS. We showed that these pathway PRS significantly predict the PD status. Furthermore, we demonstrated that Mito PRS are significantly associated with later age of onset in PD patients. Our results may add to the accumulating evidence for the contribution of mitochondrial and autophagy-lysosomal pathways to PD risk and facilitate biologically relevant risk stratification of PD patients.

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.

Alterations in Self-Aggregating Neuropeptides in Cerebrospinal Fluid of Patients with Parkinsonian Disorders

Background: Parkinson’s disease (PD), progressive supranuclear palsy (PSP), and multiple system atrophy (MSA) present with similar movement disorder symptoms but distinct protein aggregates upon pathological examination. Objective: Discovery and validation of candidate biomarkers in parkinsonian disorders for differential diagnosis of subgroup molecular etiologies. Methods: Untargeted liquid chromatography (LC)-mass spectrometry (MS) proteomics was used for discovery profiling in cerebral spinal fluid (CSF) followed by LC-MS/MS based multiple reaction monitoring for validation of candidates. We compared clinical variation within the parkinsonian cohort including PD subgroups exhibiting tremor dominance (TD) or postural instability gait disturbance and those with detectable leukocytes in CSF. Results: We have identified candidate peptide biomarkers and validated related proteins with targeted quantitative multiplexed assays. Dopamine-drug naïve patients at first diagnosis exhibit reduced levels of signaling neuropeptides, chaperones, and processing proteases for packaging of self-aggregating peptides into dense core vesicles. Distinct patterns of biomarkers were detected in the parkinsonian disorders but were not robust enough to offer a differential diagnosis. Different biomarker changes were detected in male and female patients with PD. Subgroup specific candidate biomarkers were identified for TD PD and PD patients with leukocytes detected in CSF. Conclusion: PD, MSA, and PSP exhibit overlapping as well as distinct protein biomarkers that suggest specific molecular etiologies. This indicates common sensitivity of certain populations of selectively vulnerable neurons in the brain, and distinct therapeutic targets for PD subgroups. Our report validates a decrease in CSF levels of self-aggregating neuropeptides in parkinsonian disorders and supports the role of native amyloidogenic proteins in etiologies of neurodegenerative diseases.

New Insights on Gene by Environmental Effects of Drugs of Abuse in Animal Models Using GeneNetwork

Gene-by-environment interactions are important for all facets of biology, especially behaviour. Families of isogenic strains of mice, such as the BXD strains, are excellently placed to study these interactions, as the same genome can be tested in multiple environments. BXD strains are recombinant inbred mouse strains derived from crossing two inbred strains—C57BL/6J and DBA/2J mice. Many reproducible genometypes can be leveraged, and old data can be reanalysed with new tools to produce novel insights. We obtained drug and behavioural phenotypes from Philip et al. Genes, Brain and Behaviour 2010, and reanalysed their data with new genotypes from sequencing, as well as new models (Genome-wide Efficient Mixed Model Association (GEMMA) and R/qtl2). We discovered QTLs on chromosomes 3, 5, 9, 11, and 14, not found in the original study. We reduced the candidate genes based on their ability to alter gene expression or protein function. Candidate genes included Slitrk6 and Cdk14. Slitrk6, in a Chromosome14 QTL for locomotion, was found to be part of a co-expression network involved in voluntary movement and associated with neuropsychiatric phenotypes. Cdk14, one of only three genes in a Chromosome5 QTL, is associated with handling induced convulsions after ethanol treatment, that is regulated by the anticonvulsant drug valproic acid. By using families of isogenic strains, we can reanalyse data to discover novel candidate genes involved in response to drugs of abuse.

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.

Modelling the functional genomics of Parkinson's disease in Caenorhabditis elegans: LRRK2 and beyond

For decades, Parkinson's disease (PD) cases have been genetically categorised into familial, when caused by mutations in single genes with a clear inheritance pattern in affected families, or idiopathic, in the absence of an evident monogenic determinant. Recently, genome-wide association studies (GWAS) have revealed how common genetic variability can explain up to 36% of PD heritability and that PD manifestation is often determined by multiple variants at different genetic loci. Thus, one of the current challenges in PD research stands in modelling the complex genetic architecture of this condition and translating this into functional studies. Caenorhabditis elegans provide a profound advantage as a reductionist, economical model for PD research, with a short lifecycle, straightforward genome engineering and high conservation of PD relevant neural, cellular and molecular pathways. Functional models of PD genes utilising C. elegans show many phenotypes recapitulating pathologies observed in PD. When contrasted with mammalian in vivo and in vitro models, these are frequently validated, suggesting relevance of C. elegans in the development of novel PD functional models. This review will discuss how the nematode C. elegans PD models have contributed to the uncovering of molecular and cellular mechanisms of disease, with a focus on the genes most commonly found as causative in familial PD and risk factors in idiopathic PD. Specifically, we will examine the current knowledge on a central player in both familial and idiopathic PD, Leucine-rich repeat kinase 2 (LRRK2) and how it connects to multiple PD associated GWAS candidates and Mendelian disease-causing genes.

GESLM algorithm for detecting causal SNPs in GWAS with multiple phenotypes

With the development of genome-wide association studies, how to gain information from a large scale of data has become an issue of common concern, since traditional methods are not fully developed to solve problems such as identifying loci-to-loci interactions (also known as epistasis). Previous epistatic studies mainly focused on local information with a single outcome (phenotype), while in this paper, we developed a two-stage global search algorithm, Greedy Equivalence Search with Local Modification (GESLM), to implement a global search of directed acyclic graph in order to identify genome-wide epistatic interactions with multiple outcome variables (phenotypes) in a case-control design. GESLM integrates the advantages of score-based methods and constraint-based methods to learn the phenotype-related Bayesian network and is powerful and robust to find the interaction structures that display both genetic associations with phenotypes and gene interactions. We compared GESLM with some common phenotype-related loci detecting methods in simulation studies. The results showed that our method improved the accuracy and efficiency compared with others, especially in an unbalanced case-control study. Besides, its application on the UK Biobank dataset suggested that our algorithm has great performance when handling genome-wide association data with more than one phenotype.

Common X-Chromosome Variants Are Associated with Parkinson Disease Risk

OBJECTIVE

The objective of this study was to identify genetic variants on the X-chromosome associated with Parkinson disease (PD) risk.

METHODS

We performed an X-chromosome-wide association study (XWAS) of PD risk by meta-analyzing results from sex-stratified analyses. To avoid spurious associations, we designed a specific harmonization pipeline for the X-chromosome and focused on a European ancestry sample. We included 11,142 cases, 280,164 controls, and 5,379 proxy cases, based on parental history of PD. Additionally, we tested the association of significant variants with (1) PD risk in an independent replication with 1,561 cases and 2,465 controls and (2) putamen volume in 33,360 individuals from the UK Biobank.

RESULTS

In the discovery meta-analysis, we identified rs7066890 (odds ratio [OR] = 1.10, 95% confidence interval [CI] = 1.06-1.14, p = 2.2 × 10-9 ), intron of GPM6B, and rs28602900 (OR = 1.10, 95% CI = 1.07-1.14, p = 1.6 × 10-8 ) in a high gene density region including RPL10, ATP6A1, FAM50A, and PLXNA3. The rs28602900 association with PD was replicated (OR = 1.16, 95% CI = 1.03-1.30, p = 0.016) and shown to colocalize with a significant expression quantitative locus (eQTL) regulating RPL10 expression in the putamen and other brain tissues in the Genotype-Tissue Expression Project. Additionally, the rs28602900 locus was found to be associated with reduced brain putamen volume. No results reached genome-wide significance in the sex-stratified analyses.

INTERPRETATION

We report the first XWAS of PD and identify 2 genome-wide significant loci. The rs28602900 association was replicated in an independent PD dataset and showed concordant effects in its association with putamen volume. Critically, rs26802900 is a significant eQTL of RPL10. These results support a role for ribosomal proteins in PD pathogenesis and show that the X-chromosome contributes to PD genetic risk. ANN NEUROL 2021;90:22-34.

The Genetics of Parkinson's Disease and Implications for Clinical Practice

The genetic landscape of Parkinson’s disease (PD) is characterised by rare high penetrance pathogenic variants causing familial disease, genetic risk factor variants driving PD risk in a significant minority in PD cases and high frequency, low penetrance variants, which contribute a small increase of the risk of developing sporadic PD. This knowledge has the potential to have a major impact in the clinical care of people with PD. We summarise these genetic influences and discuss the implications for therapeutics and clinical trial design.

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.

Proteomic Profile of Saliva in Parkinson's Disease Patients: A Proof of Concept Study

Parkinson’s disease (PD) is a progressive neurodegenerative disorder. It affects many organs. Lewy bodies—a histopathological “hallmark” of PD—are detected in about 75% of PD submandibular gland samples. We hypothesize that saliva can be a source of biomarkers of PD. The aim of the study was to evaluate and compare the salivary proteome of PD patients and healthy controls (HC). Salivary samples from 39 subjects (24 PD patients, mean age 61.6 ± 8.2; 15 HC, mean age 60.9 ± 6.7) were collected. Saliva was collected using RNA-Pro-Sal kits. Label-free LC-MS/MS mass spectrometry was performed to characterize the proteome of the saliva. IPA analysis of upstream inhibitors was performed. A total of 530 proteins and peptides were identified. We observed lower concentrations of S100-A16, ARP2/3, and VPS4B in PD group when compared to HC. We conclude that the salivary proteome composition of PD patients is different than that of healthy controls. We observed a lower concentration of proteins involved in inflammatory processes, exosome formation, and adipose tissue formation. The variability of expression of proteins between the two groups needs to be considered.

Biallelic mutations in calcium release activated channel regulator 2A (CRACR2A) cause a primary immunodeficiency disorder

CRAC channel regulator 2 A (CRACR2A) is a large Rab GTPase that is expressed abundantly in T cells and acts as a signal transmitter between T cell receptor stimulation and activation of the Ca²⁺-NFAT and JNK-AP1 pathways. CRACR2A has been linked to human diseases in numerous genome-wide association studies, however, to date no patient with damaging variants in CRACR2A has been identified. In this study, we describe a patient harboring biallelic variants in CRACR2A [paternal allele c.834 gaG> gaT (p.E278D) and maternal alelle c.430 Aga > Gga (p.R144G) c.898 Gag> Tag (p.E300*)], the gene encoding CRACR2A. The 33-year-old patient of East-Asian origin exhibited late onset combined immunodeficiency characterised by recurrent chest infections, panhypogammaglobulinemia and CD4+ T cell lymphopenia. In vitro exposure of patient B cells to a T-dependent stimulus resulted in normal generation of antibody-secreting cells, however the patient's T cells showed pronounced reduction in CRACR2A protein levels and reduced proximal TCR signaling, including dampened SOCE and reduced JNK phosphorylation, that contributed to a defect in proliferation and cytokine production. Expression of individual allelic mutants in CRACR2A-deleted T cells showed that the CRACR2A^E278D mutant did not affect JNK phosphorylation, but impaired SOCE which resulted in reduced cytokine production. The truncated double mutant CRACR2A^R144G/E300* showed a pronounced defect in JNK phosphorylation as well as SOCE and strong impairment in cytokine production. Thus, we have identified variants in CRACR2A that led to late-stage combined immunodeficiency characterized by loss of function in T cells.

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.

Role of Genes and Treatments for Parkinson’s Disease

Parkinson’s Disease (PD) is a complex neurodegenerative disorder that mainly results due to the loss of dopaminergic neurons in the substantia nigra of the midbrain. It is well known that dopamine is synthesized in substantia nigra and is transported to the striatumvianigrostriatal tract. Besides the sporadic forms of PD, there are also familial cases of PD and number of genes (both autosomal dominant as well as recessive) are responsible for PD. There is no permanent cure for PD and to date, L-dopa therapy is considered to be the best option besides having dopamine agonists. In the present review, we have described the genes responsible for PD, the role of dopamine, and treatment strategies adopted for controlling the progression of PD in humans.

Failure of cosegregation between a rare STAP1 missense variant and hypercholesterolemia

Autosomal dominant familial hypercholesterolemia (FH) is characterized by elevated low-density lipoprotein cholesterol levels and an increased risk for atherosclerotic cardiovascular disease. Although rare pathogenic variants in genes encoding the low-density lipoprotein receptor, apolipoprotein B, proprotein convertase subtilisin/kexin 9 are found in more than 80% of molecularly defined patients with FH, a few rare minor causative genes have been proposed, including the gene encoding signal-transducing adaptor family member 1 (STAP1). Here, we describe a patient with hypercholesterolemia and the rare heterozygous missense variant p.D207N in STAP1. However, extending the pedigree showed failure of the variant to cosegregate with hypercholesterolemia, as both his sons were carriers of the variant and both were also normolipidemic. The findings add to the evidence against STAP1 as a genetic locus for FH.

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.

Parkinsonism: A Rare Adverse Effect of Valproic Acid

Valproic acid (VPA) is an anti-epileptic drug (AED) used as a first-choice agent for most forms of epilepsy. It is used in the treatment of manic episodes, bipolar disorder, migraine prevention, and impulse control. Hence it is one of the most commonly prescribed drugs by physicians nowadays. VPA acts by increasing gama amino butyric acid (GABA) levels, and also reduces neuronal activation by blocking voltage-gated sodium, potassium, and calcium channels. VPA has various adverse effects like thrombocytopenia, hyperammonemia, teratogenicity causing spina bifida in newborns when exposed in utero. The focus of this review is to research one such easily overlooked adverse effect of VPA, which is VPA-induced Parkinsonism. We carried out a review of literature and gathered all comprehensive peer-reviewed articles from PubMed. The data for this research were collected ethically and legally after a thorough examination of the literature. Data obtained from the studies have suggested that Parkinsonism is an adverse effect of VPA. Chronic usage of VPA causes Parkinsonism. It occurs equally in males and females, more common in older people usually above the age of 55 years and not dose-dependent. According to the data obtained, all patients who developed Parkinsonism had serum levels in the therapeutic range (50-100 mcg/mL). Thus the chronic intake of maintenance dose of VPA seems to be the leading cause. The symptoms usually improve over a few weeks and fully resolve in a few months after stopping the drug. When the patient's symptoms do not improve, it means VPA has unmasked the underlying potential for developing Parkinson's disease. Such patients benefit from levodopa therapy. However, the mechanism of how VPA causes Parkinsonism remains unknown. Based on the articles reviewed, we hypothesize that VPA's mechanism of neuronal inactivation by blocking membrane channels across the neuronal membrane, primarily when used chronically could be the mechanism by which it causes Parkinsonism. VPA causes down regulation of sodium and potassium channels on neuronal membrane in order to stop the neurons from firing. Thereby a decrease in action potential across the neurons causes a temporary physiological inactivation of the neuron. When multiple neurons are inactivated in the basal ganglia of the brain, the patient develops symptoms of Parkinsonism. As the neurons are only temporarily inactivated physiologically, when the drug is stopped the membrane receptors are reactivated on the neuronal membranes. This leads to neuronal activation and neuronal membrane potential becomes the same as before. The above mechanism clarifies why the symptoms settle down when the medication is stopped.

Adaptive Fisher method detects dense and sparse signals in association analysis of SNV sets

BACKGROUND

With the development of next generation sequencing (NGS) technology and genotype imputation methods, statistical methods have been proposed to test a set of genomic variants together to detect if any of them is associated with the phenotype or disease. In practice, within the set, there is an unknown proportion of variants truly causal or associated with the disease. There is a demand for statistical methods with high power in both dense and sparse scenarios, where the proportion of causal or associated variants is large or small respectively.

RESULTS

We propose a new association test - weighted Adaptive Fisher (wAF) that can adapt to both dense and sparse scenarios by adding weights to the Adaptive Fisher (AF) method we developed before. Using simulation, we show that wAF enjoys comparable or better power to popular methods such as sequence kernel association tests (SKAT and SKAT-O) and adaptive SPU (aSPU) test. We apply wAF to a publicly available schizophrenia dataset, and successfully detect thirteen genes. Among them, three genes are supported by existing literature; six are plausible as they either relate to other neurological diseases or have relevant biological functions.

CONCLUSIONS

The proposed wAF method is a powerful disease-variants association test in both dense and sparse scenarios. Both simulation studies and real data analysis indicate the potential of wAF for new biological findings.

Association of Hypomorphic P2X7 Receptor Genotype With Age

One of the main risk factors for brain diseases is aging. Recent studies have shown that aging is a progressive degenerative process associated with chronic low-level inflammation. The ATP-gated P2X7 receptor (P2X7R) plays an important role in inflammation and has been associated with different brain (e.g., Alzheimer’s and Parkinson’s) or other age-related (osteoporosis, arthritis, cancer) diseases. Several single nucleotide polymorphisms (SNPs) in the P2RX7 gene have been identified, including the loss-of-function 1513A>C and 1405A>G SNPs, and the gain-of-function 489C>T and 1068G>A SNPs. We carried out a literature analysis to verify an association between P2RX7 SNPs’ frequency and age. In 34 worldwide eligible studies (11.858 subjects) no correlation between 1513CC genotype frequency and age emerged. On the contrary, analysis of European Caucasian cohorts (7.241 subjects) showed a significant increase in 1513CC frequency with age (P = 0.027). In agreement with these findings, analysis of two publicly available datasets, including USA Caucasian cohorts, unveiled an increased frequency of 1513CC and 489CC genotypes with age (P = 0.0055 and P = 0.0019, respectively). Thus, hypomorphic P2RX7 genotypes may be positively selected with age in European and North American Caucasian populations. We hypothesize that Caucasian individuals bearing an anti-inflammatory P2X7R phenotype and living in high-income countries may have a longer life expectancy.

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.

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.

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies

BACKGROUND

Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease.

METHODS

We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation.

FINDINGS

Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16-36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10^-7).

INTERPRETATION

These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data.

FUNDING

The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources).

Association of RIT2 and RAB7L1 with Parkinson's disease: a case-control study in a Taiwanese cohort and a meta-analysis in Asian populations

Several genome-wide association studies and meta-analyses on Parkinson's disease (PD)-related genes have identified several risk foci in Ras-related genes, particularly among Caucasian individuals. However, the corresponding results have been controversial among Asian individuals. We investigated whether 2 single-nucleotide polymorphisms of Ras-related genes, RIT2 (rs12456492) and RAB7L1 (rs823118), are associated with PD risk in Taiwanese individuals. In addition, we conducted a meta-analysis of all studies related to rs12456492 in Asian populations to resolve inconsistency in this locus. In total, 1103 Taiwanese individuals (588 patients with PD and 515 controls) and 1111 Taiwanese individuals (594 patients with PD and 517 controls) were genotyped for rs12456492 and rs823118. However, we could not confirm the association of rs12456492 and rs823118 with PD. Our current meta-analysis involving the rs12456492(A/G) variant demonstrated that the GG + GA genotypes, GG genotypes, and G allele may be risk factors for PD. RIT2 may increase PD risk in Asian individuals. The discrepancies between Caucasian and Asian populations may be due to differences in geographic region-specific genetic backgrounds and gene-environmental interactions.

Centromeric Satellite DNAs: Hidden Sequence Variation in the Human Population

The central goal of medical genomics is to understand the inherited basis of sequence variation that underlies human physiology, evolution, and disease. Functional association studies currently ignore millions of bases that span each centromeric region and acrocentric short arm. These regions are enriched in long arrays of tandem repeats, or satellite DNAs, that are known to vary extensively in copy number and repeat structure in the human population. Satellite sequence variation in the human genome is often so large that it is detected cytogenetically, yet due to the lack of a reference assembly and informatics tools to measure this variability, contemporary high-resolution disease association studies are unable to detect causal variants in these regions. Nevertheless, recently uncovered associations between satellite DNA variation and human disease support that these regions present a substantial and biologically important fraction of human sequence variation. Therefore, there is a pressing and unmet need to detect and incorporate this uncharacterized sequence variation into broad studies of human evolution and medical genomics. Here I discuss the current knowledge of satellite DNA variation in the human genome, focusing on centromeric satellites and their potential implications for disease.

Identification of Shared Genes Between Ischemic Stroke and Parkinson's Disease Using Genome-Wide Association Studies

Ischemic stroke (IS) and Parkinson's disease (PD) are two neurological diseases that often strike individuals of advanced age. Although thought of as a disease of old age, PD can occur in younger patients. In many of these cases, genetic mutations underlie the disease. As with PD, stroke can also have a genetic component. Although many of the risk factors for IS are considered to be modifiable, a significant portion is not, suggesting that some of stroke risk factors may have a genetic origin. Large-scale genome-wide association studies (GWAS) have identified several IS and PD gene variants recently. Converging epidemiologic and pathological evidence suggests that IS and PD may be linked. However, it is still unclear whether these two conditions share a common mechanism. Here, we sought to determine the genetic mechanism underlying the possible association between IS and PD. We conducted a multi-step systemic analysis comprising (1) identification of IS and PD variants validated by known GWAS, (2) two separate gene-based tests using Versatile Gene-based Association Study 2 (VEGAS2) and PLINK, (3) a transcriptome-wide association study (TWAS), and (4) analyses of gene expression using an online tool in Gene Expression Omnibus. Our investigation revealed that IS and PD have in common five shared genes: GPX7, LBH, ZCCHC10, DENND2A, and NUDT14, which pass gene-based tests. Functionally, these genes are expressed differentially in IS and PD patients compared to neurologically healthy control subjects. This genetic overlap may provide clues on how IS and PD are linked mechanistically. This new genetic insight into these two diseases may be very valuable for narrowing the focus of future studies on the genetic basis of IS and PD and for developing novel therapies.

Association between one-carbon metabolism indices and DNA methylation status in maternal and cord blood

One-carbon metabolism is essential for multiple cellular processes and can be assessed by the concentration of folate metabolites in the blood. One-carbon metabolites serve as methyl donors that are required for epigenetic regulation. Deficiencies in these metabolites are associated with a variety of poor health outcomes, including adverse pregnancy complications. DNA methylation is known to vary with one-carbon metabolite concentration, and therefore may modulate the risk of adverse pregnancy outcomes. This study addresses changes in one-carbon indices over pregnancy and the relationship between maternal and child DNA methylation and metabolite concentrations by leveraging data from 24 mother-infant dyads. Five of the 13 metabolites measured from maternal blood and methylation levels of 993 CpG sites changed over the course of pregnancy. In dyads, maternal and fetal one-carbon concentrations were highly correlated, both early in pregnancy and at delivery. The 993 CpG sites whose methylation levels changed over pregnancy in maternal blood were also investigated for associations with metabolite concentrations in infant blood at delivery, where five CpG sites were associated with the concentration of at least one metabolite. Identification of CpG sites that change over pregnancy may result in better characterization of genes and pathways involved in maintaining a healthy, term pregnancy.

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.

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.

Genome-wide CRISPR screen for PARKIN regulators reveals transcriptional repression as a determinant of mitophagy

In mitophagy, damaged mitochondria are targeted for disposal by the autophagy machinery. PARKIN promotes signaling of mitochondrial damage to the autophagy machinery for engagement, and PARKIN mutations cause Parkinson’s disease, possibly because damaged mitochondria accumulate in neurons. Because regulation of PARKIN abundance and the impact on signaling are poorly understood, we performed a genetic screen to identify PARKIN abundance regulators. Both positive and negative regulators were identified and will help us to further understand mitophagy and Parkinson’s disease. We show that some of the identified genes negatively regulate PARKIN gene expression, which impacts signaling of mitochondrial damage in mitophagy. This link between transcriptional repression and mitophagy is also apparent in neurons in culture, bearing implications for disease.

PARKIN, an E3 ligase mutated in familial Parkinson’s disease, promotes mitophagy by ubiquitinating mitochondrial proteins for efficient engagement of the autophagy machinery. Specifically, PARKIN-synthesized ubiquitin chains represent targets for the PINK1 kinase generating phosphoS65-ubiquitin (pUb), which constitutes the mitophagy signal. Physiological regulation of PARKIN abundance, however, and the impact on pUb accumulation are poorly understood. Using cells designed to discover physiological regulators of PARKIN abundance, we performed a pooled genome-wide CRISPR/Cas9 knockout screen. Testing identified genes individually resulted in a list of 53 positive and negative regulators. A transcriptional repressor network including THAP11 was identified and negatively regulates endogenous PARKIN abundance. RNAseq analysis revealed the PARKIN-encoding locus as a prime THAP11 target, and THAP11 CRISPR knockout in multiple cell types enhanced pUb accumulation. Thus, our work demonstrates the critical role of PARKIN abundance, identifies regulating genes, and reveals a link between transcriptional repression and mitophagy, which is also apparent in human induced pluripotent stem cell-derived neurons, a disease-relevant cell type.

DLG2, but not TMEM229B, GPNMB, and ITGA8 polymorphism, is associated with Parkinson's disease in a Taiwanese population

Transmembrane or membrane-associated protein dysfunction is increasingly recognized as an important mechanism of pathogenesis in Parkinson's disease (PD). Previous genome-wide association studies and their meta-analysis in PD genes have identified several risk foci in transmembrane protein-encoding genes. Herein, we investigated the effect of 4 such PD-associated genetic variants reported in Caucasians, including discs-large membrane-associated guanylate kinase scaffolding protein 2 (DLG2 rs3793947), transmembrane protein 229B (TMEM229B rs1555399), glycoprotein nonmetastatic melanoma protein B (GPNMB rs199347), and integrin subunit alpha 8 (ITGA8 rs7077361). A total of 1185 Taiwanese subjects comprising 592 PD patients and 593 unrelated age-matched controls were genotyped. DLG2 rs3793947 AA genotype showed a significantly lower prevalence in female PD patients compared to the female controls (p = 0.019). The recessive model analysis also demonstrated a reduced PD risk for females in AA genotype (odds ratio = 0.573, 95% confidence interval: 0.379-0.868, p = 0.008). The frequencies of TMEM229B rs1555399 and GPNMB rs199347 genotypes and alleles were similar in PD patients and controls. ITG8 rs7077361 was not polymorphic in all subjects of this study. These data suggested that DLG2, but not TMEM229B, GPNMB, and ITGA8, influenced the risk of PD in Taiwan.

Deregulation of α-synuclein in Parkinson's disease: Insight from epigenetic structure and transcriptional regulation of SNCA

Understanding regulation of α-synuclein has long been a central focus for Parkinson's disease (PD) researchers. Accumulation of this protein in the Lewy body or neurites, mutations in the coding region of the gene and strong association of α-synuclein encoding gene multiplication (duplication/triplication) with familial form of PD have indicated the importance of this molecule in pathogenesis of the disease. Several years of research identified many potential faulty pathways associated with accumulation of α-synuclein inside dopaminergic neurons and its transmission to neighboring ones. Concurrently, an appreciable body of research is growing to understand the epigenetic and genetic deregulation of α-synuclein that might contribute to the disease pathology. Completion of the ENCODE (Encyclopedia of DNA Elements) project and recent advancement made in the epigenetic and trans factor mediated regulation of each gene, has tremendously accelerated the need to carefully understand the epigenetic structure of the gene (SNCA) encoding α-synuclein protein in order to decipher the regulation and contribution of α-synuclein to the pathogenesis of PD. We have also analyzed the detailed epigenetic structure of this gene with knowledge from ENCODE database, which may open new avenues in α-synuclein research. Interestingly, we have found that the gene contains several transcriptionally activate histone modifications and associated potential transcription factor binding sites in the non-coding areas that strongly suggest alternative regulatory pathways. Altogether this review will provide interesting insight of α-synuclein gene regulation from epigenetic, genetic and post-transcriptional perspectives and their potential implication in the PD pathogenesis.

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.

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.

Genome-wide pathway-based association analysis identifies risk pathways associated with Parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disease. It is generally believed that it is influenced by both genetic and environmental factors, but the precise pathogenesis of PD is unknown to date. In this study, we performed a pathway analysis based on genome-wide association study (GWAS) to detect risk pathways of PD in three GWAS datasets. We first mapped all SNP markers to autosomal genes in each GWAS dataset. Then, we evaluated gene risk values using the minimum P-value of the tagSNPs. We took a pathway as a unit to identify the risk pathways based on the cumulative risks of the genes in the pathway. Finally, we combine the analysis results of the three datasets to detect the high risk pathways associated with PD. We found there were five same pathways in the three datasets. Besides, we also found there were five pathways which were shared in two datasets. Most of these pathways are associated with nervoussystem. Five pathways had been reported to be PD-related pathways in the previous literature. Our findings also implied that there was a close association between immune response and PD. Continued investigation of these pathways will further help us explain the pathogenesis of PD.