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More than 800 published genetic association studies have implicated dozens of potential risk loci in Parkinson's disease (PD). To facilitate the interpretation of these findings, we have created a dedicated online resource, PDGene, that comprehensively collects and meta-analyzes all published studies in the field. A systematic literature screen of -27,000 articles yielded 828 eligible articles from which relevant data were extracted. In addition, individual-level data from three publicly available genome-wide association studies (GWAS) were obtained and subjected to genotype imputation and analysis. Overall, we performed meta-analyses on more than seven million polymorphisms originating either from GWAS datasets and/or from smaller scale PD association studies. Meta-analyses on 147 SNPs were supplemented by unpublished GWAS data from up to 16,452 PD cases and 48,810 controls. Eleven loci showed genome-wide significant (P < 5 × 10(-8)) association with disease risk: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25. In addition, we identified novel evidence for genome-wide significant association with a polymorphism in ITGA8 (rs7077361, OR 0.88, P = 1.3 × 10(-8)). All meta-analysis results are freely available on a dedicated online database (www.pdgene.org), which is cross-linked with a customized track on the UCSC Genome Browser. Our study provides an exhaustive and up-to-date summary of the status of PD genetics research that can be readily scaled to include the results of future large-scale genetics projects, including next-generation sequencing studies.

We recently described a genomic pathway approach to study complex diseases. We demonstrated that models constructed using single nucleotide polymorphisms (SNPs) within axon guidance pathway genes were highly predictive of Parkinson disease (PD) susceptibility, survival free of PD, and age at onset of PD within two independent whole-genome association datasets. We also demonstrated that several axon guidance pathway genes represented by SNPs within our final models were differentially expressed in PD.Here we employed our genomic pathway approach to analyze data from a whole-genome association dataset of amyotrophic lateral sclerosis (ALS); and demonstrated that models constructed using SNPs within axon guidance pathway genes were highly predictive of ALS susceptibility (odds ratio = 1739.73, p = 2.92x10(-60)), survival free of ALS (hazards ratio = 149.80, p = 1.25x10(-74)), and age at onset of ALS (R(2) = 0.86, p = 5.96x10(-66)). We also extended our analyses of a whole-genome association dataset of PD, which shared 320,202 genomic SNPs in common with the whole-genome association dataset of ALS. We compared for ALS and PD the genes represented by SNPs in the final models for susceptibility, survival free of disease, and age at onset of disease and noted that 52.2%, 37.8%, and 34.9% of the genes were shared respectively.Our findings for the axon guidance pathway and ALS have prior biological plausibility, overlap partially with PD, and may provide important insight into the causes of these and related neurodegenerative disorders.

While major inroads have been made in identifying the genetic causes of rare Mendelian disorders, little progress has been made in the discovery of common gene variations that predispose to complex diseases. The single gene variants that have been shown to associate reproducibly with complex diseases typically have small effect sizes or attributable risks. However, the joint actions of common gene variants within pathways may play a major role in predisposing to complex diseases (the paradigm of complex genetics). The goal of this study was to determine whether polymorphism in a candidate pathway (axon guidance) predisposed to a complex disease (Parkinson disease [PD]). We mined a whole-genome association dataset and identified single nucleotide polymorphisms (SNPs) that were within axon-guidance pathway genes. We then constructed models of axon-guidance pathway SNPs that predicted three outcomes: PD susceptibility (odds ratio = 90.8, p = 4.64 x 10(-38)), survival free of PD (hazards ratio = 19.0, p = 5.43 x 10(-48)), and PD age at onset (R(2) = 0.68, p = 1.68 x 10(-51)). By contrast, models constructed from thousands of random selections of genomic SNPs predicted the three PD outcomes poorly. Mining of a second whole-genome association dataset and mining of an expression profiling dataset also supported a role for many axon-guidance pathway genes in PD. These findings could have important implications regarding the pathogenesis of PD. This genomic pathway approach may also offer insights into other complex diseases such as Alzheimer disease, diabetes mellitus, nicotine and alcohol dependence, and several cancers.

BACKGROUND: Genome-wide association studies hold substantial promise for identifying common genetic variants that regulate susceptibility to complex diseases. However, for the detection of small genetic effects, single studies may be underpowered. Power may be improved by combining genome-wide datasets with meta-analytic techniques. METHODOLOGY/PRINCIPAL FINDINGS: Both single and two-stage genome-wide data may be combined and there are several possible strategies. In the two-stage framework, we considered the options of (1) enhancement of replication data and (2) enhancement of first-stage data, and then, we also considered (3) joint meta-analyses including all first-stage and second-stage data. These strategies were examined empirically using data from two genome-wide association studies (three datasets) on Parkinson disease. In the three strategies, we derived 12, 5, and 49 single nucleotide polymorphisms that show significant associations at conventional levels of statistical significance. None of these remained significant after conservative adjustment for the number of performed analyses in each strategy. However, some may warrant further consideration: 6 SNPs were identified with at least 2 of the 3 strategies and 3 SNPs [rs1000291 on chromosome 3, rs2241743 on chromosome 4 and rs3018626 on chromosome 11] were identified with all 3 strategies and had no or minimal between-dataset heterogeneity (I(2) = 0, 0 and 15%, respectively). Analyses were primarily limited by the suboptimal overlap of tested polymorphisms across different datasets (e.g., only 31,192 shared polymorphisms between the two tier 1 datasets). CONCLUSIONS/SIGNIFICANCE: Meta-analysis may be used to improve the power and examine the between-dataset heterogeneity of genome-wide association studies. Prospective designs may be most efficient, if they try to maximize the overlap of genotyping platforms and anticipate the combination of data across many genome-wide association studies.

Abstract Objective The objective of this study was to examine clinicians' patient selection and result interpretation of a clinically validated mass spectrometry test measuring amyloid beta and ApoE blood biomarkers combined with patient age (PrecivityAD® blood test) in symptomatic patients evaluated for Alzheimer's disease (AD) or other causes of cognitive decline. Methods The Quality Improvement and Clinical Utility PrecivityAD Clinician Survey (QUIP I, ClinicalTrials.gov Identifier: NCT05477056) was a prospective, single‐arm cohort study among 366 patients evaluated by neurologists and other cognitive specialists. Participants underwent blood biomarker testing and received an amyloid probability score (APS), indicating the likelihood of a positive result on an amyloid positron emission tomography (PET) scan. The primary study outcomes were appropriateness of patient selection as well as result interpretation associated with PrecivityAD blood testing. Results A 95% (347/366) concordance rate was noted between clinicians' patient selection and the test's intended use criteria. In the final analysis including these 347 patients (median age 75 years, 56% women), prespecified test result categories incorporated 133 (38%) low APS, 162 (47%) high APS, and 52 (15%) intermediate APS patients. Clinicians' pretest and posttest AD diagnosis probability changed from 58% to 23% in low APS patients and 71% to 89% in high APS patients (p

Objective: To establish, apply, and evaluate a computable phenotype for the recruitment of individuals with successful cognitive aging. Participants and Methods: Interviews with 10 aging experts identified electronic health record (EHR)-available variables representing successful aging among individuals aged 85 years and older. On the basis of the identified variables, we developed a rule-based computable phenotype algorithm composed of 17 eligibility criteria. Starting September 1, 2019, we applied the computable phenotype algorithm to all living persons aged 85 years and older at the University of Florida Health, which identified 24,024 individuals. This sample was comprised of 13,841 (58%) women, 13,906 (58%) Whites, and 16,557 (69%) non-Hispanics. A priori permission to be contacted for research had been obtained for 11,898 individuals, of whom 470 responded to study announcements and 333 consented to evaluation. Then, we contacted those who consented to evaluate whether their cognitive and functional status clinically met out successful cognitive aging criteria of a modified Telephone Interview for Cognitive Status score of more than 27 and Geriatric Depression Scale of less than 6. The study was completed on December 31, 2022. Results: Of the 45% of living persons aged 85 years and older included in the University of Florida Health EHR database identified by the computable phenotype as successfully aged, approximately 4% of these responded to study announcements and 333 consented, of which 218 (65%) met successful cognitive aging criteria through direct evaluation. Conclusion: The study evaluated a computable phenotype algorithm for the recruitment of individuals for a successful aging study using large-scale EHRs. Our study provides proof of concept of using big data and informatics as aids for the recruitment of individuals for prospective cohort studies.

The electronic medical record (EMR) presents an opportunity to standardize patient data collection based on quality guidelines and conduct practice-based research. We describe the development of a customized EMR “toolkit” that standardizes patient data collection with hundreds of discrete fields that supports Best Practices for treating patients with memory disorders. The toolkit also supports practice-based research. We describe the design and successful implementation of a customized EMR toolkit to support Best Practices in the care of patients with memory disorders. We discuss applications, including quality improvement projects and current research initiatives, using the toolkit. This toolkit is being shared with other departments of Neurology as part of the Neurology Practice-Based Research Network. Data collection is ongoing, including longitudinal follow-up. This toolkit will generate data that will allow for descriptive and hypothesis driven research as well-quality improvement among patients seen in a memory clinic.

We report on an evaluation of coding variants within the parkin gene to assess their frequency in a North American clinical series of 313 Parkinson's disease (PD) cases and 192 unrelated controls. We hypothesized that the carrier frequency of parkin coding mutations, exon deletions, or duplications may be greater in PD cases. However, point mutations and exonic deletions/duplications, reported previously as pathogenic in homozygous or compound heterozygous individuals, occurred in both cases and controls with similar frequencies (3.8% in cases, 3.1% in controls). Furthermore, only stratified subanalyses detected any genetic association between the V380L common coding polymorphism and PD. We discuss the implication of parkin mutations for Parkinson's disease from this population perspective. © 2003 Movement Disorder Society [ABSTRACT FROM AUTHOR]

We conducted a case-control study of the α-synuclein–interacting protein gene (SNCAIP, also known as synphilin-1) and Parkinson's disease (PD). A total of 319 PD cases and 195 controls were genotyped for four SNCAIP variants, including a microsatellite repeat in intron 4 and three restriction fragment length polymorphisms (RFLP) proximal to the 5' terminal of exons 1, 4, and 6. None of the variants were found associated with PD overall. Global score statistics were not significant for four, three, and two loci haplotypes. All four loci were in linkage disequilibrium for cases, controls, or both groups combined (P < 0.0001). Recursive partitioning showed no interactions between variants of the SNCAIP gene and variants of the α-synuclein gene (SNCA) or the parkin (PARK2) gene. © 2003 Movement Disorder Society [ABSTRACT FROM AUTHOR]

The article presents a response by doctor Jim Maraganore to a letter to the editor about his study on new genetic association data for 13 SNPs reported by him to be potentially associated with Parkinson disease in a previous issue.

Objective: To develop and implement a customized toolkit within the electronic medical record (EMR) to standardize care of patients with brain tumors. Patients and Methods: We built a customized structured clinical documentation support toolkit to capture standardized data at office visits. We detail the process by which this toolkit was conceptualized and developed. Toolkit development was a physician-led process to determine a work flow and necessary elements to support best practices as defined by the neuro-oncology clinical team. Results: We have developed in our EMR system a customized work flow for clinical encounters with neuro-oncology patients. In addition to providing a road map for clinical care by our neuro-oncology team, the toolkit is designed to maximize discrete data capture. Several hundred fields of discrete data are captured through the toolkit in the context of our routine office visits. We describe the characteristics of patients seen at our clinic, the adoption of the toolkit, current initiatives supported by the toolkit, and future applications. Conclusion: The EMR can be effectively structured to standardize office visits and improve discrete data capture. This toolkit can be leveraged to support quality improvement and practice-based research initiatives at the point of care in a neuro-oncology practice.

Abstract Background We developed and implemented a structured clinical documentation support (SCDS) toolkit within the electronic medical record, to optimize patient care, facilitate documentation, and capture data at office visits in a sleep medicine/neurology clinic for patient care and research collaboration internally and with other centers. Methods To build our SCDS toolkit, physicians met frequently to develop content, define the cohort, select outcome measures, and delineate factors known to modify disease progression. We assigned tasks to the care team and mapped data elements to the progress note. Programmer analysts built and tested the SCDS toolkit, which included several score tests. Auto scored and interpreted tests included the Generalized Anxiety Disorder 7-item, Center for Epidemiological Studies Depression Scale, Epworth Sleepiness Scale, Pittsburgh Sleep Quality Index, Insomnia Severity Index, and the International Restless Legs Syndrome Study Group Rating Scale. The SCDS toolkits also provided clinical decision support (untreated anxiety or depression) and prompted enrollment of patients in a DNA biobank. Results The structured clinical documentation toolkit captures hundreds of fields of discrete data at each office visit. This data can be displayed in tables or graphical form. Best practice advisories within the toolkit alert physicians when a quality improvement opportunity exists. As of May 1, 2019, we have used the toolkit to evaluate 18,105 sleep patients at initial visit. We are also collecting longitudinal data on patients who return for annual visits using the standardized toolkits. We provide a description of our development process and screenshots of our toolkits. Conclusions The electronic medical record can be structured to standardize Sleep Medicine office visits, capture data, and support multicenter quality improvement and practice-based research initiatives for sleep patients at the point of care.

Objective: We describe our experience with routinely capturing and analyzing Mediterranean diet data via structured clinical documentation support tools built into the electronic medical record and describe adherence to the Mediterranean diet in patients at risk for either stroke or dementia in a US neurology clinical practice. Patients and Methods: The Mediterranean diet is associated with a reduced risk of stroke and dementia. The Department of Neurology at NorthShore University HealthSystem routinely evaluates patients at initial and annual outpatient visits using structured clinical documentation support (SCDS) tools built into the electronic medical record (EMR). For patient evaluations in our Vascular Neurology and Brain Health subspecialty clinics, SCDS tools in the EMR include the validated 14-item questionnaire for Mediterranean diet adherence (PREvención con DIeta MEDiterránea [PREDIMED]) that autoscores, auto-interprets, writes to the progress note, and electronically captures data. Our study population includes patients seen at these clinics from July 1, 2015, through November 29, 2017. Results: At their initial office visit, 25.5% (95/373) of Brain Health patients scored 10 or more points (“strongly adherent”) on the PREDIMED (median, 8; range, 0-14) whereas 6.7% (55/829) of Vascular Neurology patients achieved a score of 10 or more points (median, 6; range, 0-12). By contrast, 34.7% (2586/7447) of individuals in the original PREDIMED cohort were strongly adherent to the Mediterranean diet. Conclusion: PREDIMED scores can be electronically captured to tailor nutrition interventions by assessing baseline adherence at the time of their initial neurology clinic visit. Patients in our Midwestern US clinics were weakly adherent to the Mediterranean diet. This suggests a major opportunity for nutrition intervention and education in US neurology clinical practices, toward preserving and improving brain health.

The purpose of this paper is to provide the rationale for therapeutic silencing of the alpha-synuclein gene (SNCA) in Parkinson’s disease (PD). The paper reviews the public health significance of PD; the causal links between rare SNCA variants and familial PD; the association of common SNCA variants and PD susceptibility; the association of SNCA variants also with age at onset and motor and cognitive outcomes in PD; therapeutic strategies targeting SNCA in PD; and preliminary findings and considerations on small interfering RNA-based therapies and PD.