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1.  Diagnosis of Parkinson’s disease on the basis of clinical–genetic classification: a population-based modelling study 
The Lancet. Neurology  2015;14(10):1002-1009.
Accurate diagnosis and early detection of complex disease has the potential to be of enormous benefit to clinical trialists, patients, and researchers alike. We sought to create a non-invasive, low-cost, and accurate classification model for diagnosing Parkinson’s disease risk to serve as a basis for future disease prediction studies in prospective longitudinal cohorts.
We developed a simple disease classifying model within 367 patients with Parkinson’s disease and phenotypically typical imaging data and 165 controls without neurological disease of the Parkinson’s Progression Marker Initiative (PPMI) study. Olfactory function, genetic risk, family history of PD, age and gender were algorithmically selected as significant contributors to our classifying model. This model was developed using the PPMI study then tested in 825 patients with Parkinson’s disease and 261 controls from five independent studies with varying recruitment strategies and designs including the Parkinson’s Disease Biomarkers Program (PDBP), Parkinson’s Associated Risk Study (PARS), 23andMe, Longitudinal and Biomarker Study in PD (LABS-PD), and Morris K. Udall Parkinson’s Disease Research Center of Excellence (Penn-Udall).
Our initial model correctly distinguished patients with Parkinson’s disease from controls at an area under the curve (AUC) of 0.923 (95% CI = 0.900 – 0.946) with high sensitivity (0.834, 95% CI = 0.711 – 0.883) and specificity (0.903, 95% CI = 0.824 – 0.946) in PPMI at its optimal AUC threshold (0.655). The model is also well-calibrated with all Hosmer-Lemeshow simulations suggesting that when parsed into random subgroups, the actual data mirrors that of the larger expected data, demonstrating that our model is robust and fits well. Likewise external validation shows excellent classification of PD with AUCs of 0.894 in PDBP, 0.998 in PARS, 0.955 in 23andMe, 0.929 in LABS-PD, and 0.939 in Penn-Udall. Additionally, when our model classifies SWEDD as PD, they convert within one year to typical PD more than would be expected by chance, with 4 out of 17 classified as PD converting to PD during brief follow-up; while SWEDD not classified as PD showed one conversion to PD out of 38 participants (test of proportions, p-value = 0.003).
This model may serve as a basis for future investigations into the classification, prediction and treatment of Parkinson’s disease, particularly those planning on attempting to identify prodromal or preclinical etiologically typical PD cases in prospective cohorts for efficient interventional and biomarker studies.
Please see the acknowledgements and funding section at the end of the manuscript.
PMCID: PMC4575273  PMID: 26271532
2.  The BioFIND study: Characteristics of a clinically typical Parkinson's disease biomarker cohort 
Movement Disorders  2016;31(6):924-932.
Identifying PD‐specific biomarkers in biofluids will greatly aid in diagnosis, monitoring progression, and therapeutic interventions. PD biomarkers have been limited by poor discriminatory power, partly driven by heterogeneity of the disease, variability of collection protocols, and focus on de novo, unmedicated patients. Thus, a platform for biomarker discovery and validation in well‐characterized, clinically typical, moderate to advanced PD cohorts is critically needed.
BioFIND (Fox Investigation for New Discovery of Biomarkers in Parkinson's Disease) is a cross‐sectional, multicenter biomarker study that established a repository of clinical data, blood, DNA, RNA, CSF, saliva, and urine samples from 118 moderate to advanced PD and 88 healthy control subjects. Inclusion criteria were designed to maximize diagnostic specificity by selecting participants with clinically typical PD symptoms, and clinical data and biospecimen collection utilized standardized procedures to minimize variability across sites.
We present the study methodology and data on the cohort's clinical characteristics. Motor scores and biospecimen samples including plasma are available for practically defined off and on states and thus enable testing the effects of PD medications on biomarkers. Other biospecimens are available from off state PD assessments and from controls.
Our cohort provides a valuable resource for biomarker discovery and validation in PD. Clinical data and biospecimens, available through The Michael J. Fox Foundation for Parkinson's Research and the National Institute of Neurological Disorders and Stroke, can serve as a platform for discovering biomarkers in clinically typical PD and comparisons across PD's broad and heterogeneous spectrum. © 2016 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society
PMCID: PMC5021110  PMID: 27113479
biomarkers; cerebrospinal fluid; DNA; plasma; RNA; saliva; urine
3.  Parkinson’s Disease and α-synuclein Expression 
Genetic studies of Parkinson’s disease over the last decade or more have revolutionized our understanding of this condition. α-Synuclein was the first gene to be linked to Parkinson’s disease, and is arguably the most important: the protein is the principal constituent of Lewy bodies, and variation at its locus is the major genetic risk factor for sporadic disease. Intriguingly, duplications and triplications of the locus, as well as point mutations, cause familial disease. Therefore, subtle alterations of α-synuclein expression can manifest with a dramatic phenotype. We outline the clinical impact of α-synuclein locus multiplications, and the implications that this has for Parkinson’s disease pathogenesis. Finally, we discuss potential strategies for disease-modifying therapies for this currently incurable disorder.
PMCID: PMC4669565  PMID: 21887711
Parkinsonism; genetics; α-synuclein
4.  Etiologic Ischemic Stroke Phenotypes in the NINDS Stroke Genetics Network 
Background and Purpose
NINDS Stroke Genetics Network (SiGN) is an international consortium of ischemic stroke studies that aims to generate high quality phenotype data to identify the genetic basis of etiologic stroke subtypes. This analysis characterizes the etiopathogenetic basis of ischemic stroke and reliability of stroke classification in the consortium.
Fifty-two trained and certified adjudicators determined both phenotypic (abnormal test findings categorized in major etiologic groups without weighting towards the most likely cause) and causative ischemic stroke subtypes in 16,954 subjects with imaging-confirmed ischemic stroke from 12 US studies and 11 studies from 8 European countries using the web-based Causative Classification of Stroke System. Classification reliability was assessed with blinded re-adjudication of 1509 randomly selected cases.
The distribution of etiologic categories varied by study, age, sex, and race (p<0.001 for each). Overall, only 40% to 54% of cases with a given major ischemic stroke etiology (phenotypic subtype) were classified into the same final causative category with high confidence. There was good agreement for both causative (kappa 0.72, 95%CI:0.69-0.75) and phenotypic classifications (kappa 0.73, 95%CI:0.70-0.75).
This study demonstrates that etiologic subtypes can be determined with good reliability in studies that include investigators with different expertise and background, institutions with different stroke evaluation protocols and geographic location, and patient populations with different epidemiological characteristics. The discordance between phenotypic and causative stroke subtypes highlights the fact that the presence of an abnormality in a stroke patient does not necessarily mean that it is the cause of stroke.
PMCID: PMC4286169  PMID: 25378430
etiology; classification; phenotype; stroke subtype
5.  Agreement between TOAST and CCS ischemic stroke classification 
Neurology  2014;83(18):1653-1660.
The objective of this study was to assess the level of agreement between stroke subtype classifications made using the Trial of Org 10172 Acute Stroke Treatment (TOAST) and Causative Classification of Stroke (CCS) systems.
Study subjects included 13,596 adult men and women accrued from 20 US and European genetic research centers participating in the National Institute of Neurological Disorders and Stroke (NINDS) Stroke Genetics Network (SiGN). All cases had independently classified TOAST and CCS stroke subtypes. Kappa statistics were calculated for the 5 major ischemic stroke subtypes common to both systems.
The overall agreement between TOAST and CCS was moderate (agreement rate, 70%; κ = 0.59, 95% confidence interval [CI] 0.58–0.60). Agreement varied widely across study sites, ranging from 28% to 90%. Agreement on specific subtypes was highest for large-artery atherosclerosis (κ = 0.71, 95% CI 0.69–0.73) and lowest for small-artery occlusion (κ = 0.56, 95% CI 0.54–0.58).
Agreement between TOAST and CCS diagnoses was moderate. Caution is warranted when comparing or combining results based on the 2 systems. Replication of study results, for example, genome-wide association studies, should utilize phenotypes determined by the same classification system, ideally applied in the same manner.
PMCID: PMC4223086  PMID: 25261504
6.  Stroke Genetics Network (SiGN) Study: Design and rationale for a genome-wide association study of ischemic stroke subtypes 
Background and Purpose
Meta-analyses of extant genome-wide data illustrate the need to focus on subtypes of ischemic stroke for gene discovery. The NINDS Stroke Genetics Network (SiGN) contributes substantially to meta-analyses that focus on specific subtypes of stroke.
The NINDS Stroke Genetics Network (SiGN) includes ischemic stroke cases from 24 Genetic Research Centers (GRCs), 13 from the US and 11 from Europe. Investigators harmonize ischemic stroke phenotyping using the web-based Causative Classification of Stroke (CCS) system, with data entered by trained and certified adjudicators at participating GRCs. Through the Center for Inherited Diseases Research (CIDR), SiGN plans to genotype 10,296 carefully phenotyped stroke cases using genome-wide SNP arrays, and add to these another 4,253 previously genotyped cases for a total of 14,549 cases. To maximize power for subtype analyses, the study allocates genotyping resources almost exclusively to cases. Publicly available studies provide most of the control genotypes. CIDR-generated genotypes and corresponding phenotypic data will be shared with the scientific community through dbGaP, and brain MRI studies will be centrally archived.
The SiGN consortium, with its emphasis on careful and standardized phenotyping of ischemic stroke and stroke subtypes, provides an unprecedented opportunity to uncover genetic determinants of ischemic stroke.
PMCID: PMC4056331  PMID: 24021684
ischemic stroke; genetics; genomics
7.  Using previously genotyped controls in genome-wide association studies (GWAS): application to the Stroke Genetics Network (SiGN) 
Genome-wide association studies (GWAS) are widely applied to identify susceptibility loci for a variety of diseases using genotyping arrays that interrogate known polymorphisms throughout the genome. A particular strength of GWAS is that it is unbiased with respect to specific genomic elements (e.g., coding or regulatory regions of genes), and it has revealed important associations that would have never been suspected based on prior knowledge or assumptions. To date, the discovered SNPs associated with complex human traits tend to have small effect sizes, requiring very large sample sizes to achieve robust statistical power. To address these issues, a number of efficient strategies have emerged for conducting GWAS, including combining study results across multiple studies using meta-analysis, collecting cases through electronic health records, and using samples collected from other studies as controls that have already been genotyped and made publicly available (e.g., through deposition of de-identified data into dbGaP or EGA). In certain scenarios, it may be attractive to use already genotyped controls and divert resources to standardized collection, phenotyping, and genotyping of cases only. This strategy, however, requires that careful attention be paid to the choice of “public controls” and to the comparability of genetic data between cases and the public controls to ensure that any allele frequency differences observed between groups is attributable to locus-specific effects rather than to a systematic bias due to poor matching (population stratification) or differential genotype calling (batch effects). The goal of this paper is to describe some of the potential pitfalls in using previously genotyped control data. We focus on considerations related to the choice of control groups, the use of different genotyping platforms, and approaches to deal with population stratification when cases and controls are genotyped across different platforms.
PMCID: PMC4010766  PMID: 24808905
genome-wide association study; case-control study; genetic association study; population stratification; power
8.  Genomic investigation of α-Synuclein multiplication and parkinsonism 
Annals of neurology  2008;63(6):10.1002/ana.21380.
Copy number variation is a common polymorphic phenomenon within the human genome. While the majority of these events are non-deleterious they can also be highly pathogenic. Herein we characterize five families with parkinsonism that have been identified to harbor multiplication of the chromosomal 4q21 locus containing the α-synuclein gene (SNCA).
A methodological approach employing fluorescent in situ hybridization (FISH) and Affymetrix 250K SNP microarrays (CHIPs) was used to characterize the multiplication in each family and identify the genes encoded within the region. The telomeric and centromeric breakpoints of each family were further narrowed using semi-quantitative PCR with microsatellite markers and then screened for transposable repeat elements.
The severity of clinical presentation is correlated with SNCA dosage and does not appear to be overtly effected by the presence of other genes in the multiplicated region. With the exception of the Lister kindred, in each family the multiplication event appears de novo. The type and position of Alu/LINE repeats are also different at each breakpoint. Microsatellite analysis demonstrates two genomic mechanisms are responsible for chromosome 4q21 multiplications, including both SNCA duplication and triplication.
SNCA dosage is responsible for parkinsonism, autonomic dysfunction and dementia observed within each family. We hypothesize dysregulated expression of wild-type α-synuclein results in parkinsonism and may explain the recent association of common SNCA variants in sporadic Parkinson’s disease. SNCA genomic duplication results from intra-allelic (segmental duplication) or inter-allelic recombination with unequal crossing-over, whereas both mechanisms appear to be required for genomic SNCA triplication.
PMCID: PMC3850281  PMID: 18571778
Parkinsonism; SNCA; Genomic multiplication; Alu repeat; Parkinson’s disease
10.  Creation of an Open-Access, Mutation-Defined Fibroblast Resource for Neurological Disease Research 
PLoS ONE  2012;7(8):e43099.
Our understanding of the molecular mechanisms of many neurological disorders has been greatly enhanced by the discovery of mutations in genes linked to familial forms of these diseases. These have facilitated the generation of cell and animal models that can be used to understand the underlying molecular pathology. Recently, there has been a surge of interest in the use of patient-derived cells, due to the development of induced pluripotent stem cells and their subsequent differentiation into neurons and glia. Access to patient cell lines carrying the relevant mutations is a limiting factor for many centres wishing to pursue this research. We have therefore generated an open-access collection of fibroblast lines from patients carrying mutations linked to neurological disease. These cell lines have been deposited in the National Institute for Neurological Disorders and Stroke (NINDS) Repository at the Coriell Institute for Medical Research and can be requested by any research group for use in in vitro disease modelling. There are currently 71 mutation-defined cell lines available for request from a wide range of neurological disorders and this collection will be continually expanded. This represents a significant resource that will advance the use of patient cells as disease models by the scientific community.
PMCID: PMC3428297  PMID: 22952635
11.  Parkinson's disease induced pluripotent stem cells with triplication of the α-synuclein locus 
Nature Communications  2011;2:440-.
A major barrier to research on Parkinson's disease is inaccessibility of diseased tissue for study. One solution is to derive induced pluripotent stem cells from patients and differentiate them into neurons affected by disease. Triplication of SNCA, encoding α-synuclein, causes a fully penetrant, aggressive form of Parkinson's disease with dementia. α-Synuclein dysfunction is the critical pathogenic event in Parkinson's disease, multiple system atrophy and dementia with Lewy bodies. Here we produce multiple induced pluripotent stem cell lines from an SNCA triplication patient and an unaffected first-degree relative. When these cells are differentiated into midbrain dopaminergic neurons, those from the patient produce double the amount of α-synuclein protein as neurons from the unaffected relative, precisely recapitulating the cause of Parkinson's disease in these individuals. This model represents a new experimental system to identify compounds that reduce levels of α-synuclein, and to investigate the mechanistic basis of neurodegeneration caused by α-synuclein dysfunction.
Pluripotent stem cells can be generated from the somatic cells of humans and are a useful model to study disease. Here, pluripotent stem cells are made from a patient with familial Parkinson's disease, and the resulting neurons exhibit elevated levels of α-synuclein, recapitulating the molecular features of the patient's disease.
PMCID: PMC3265381  PMID: 21863007
12.  CoAIMs: A Cost-Effective Panel of Ancestry Informative Markers for Determining Continental Origins 
PLoS ONE  2010;5(10):e13443.
Genetic ancestry is known to impact outcomes of genotype-phenotype studies that are designed to identify risk for common diseases in human populations. Failure to control for population stratification due to genetic ancestry can significantly confound results of disease association studies. Moreover, ancestry is a critical factor in assessing lifetime risk of disease, and can play an important role in optimizing treatment. As modern medicine moves towards using personal genetic information for clinical applications, it is important to determine genetic ancestry in an accurate, cost-effective and efficient manner. Self-identified race is a common method used to track and control for population stratification; however, social constructs of race are not necessarily informative for genetic applications. The use of ancestry informative markers (AIMs) is a more accurate method for determining genetic ancestry for the purposes of population stratification.
Methodology/Principal Findings
Here we introduce a novel panel of 36 microsatellite (MSAT) AIMs that determines continental admixture proportions. This panel, which we have named Continental Ancestry Informative Markers or CoAIMs, consists of MSAT AIMs that were chosen based upon their measure of genetic variance (Fst), allele frequencies and their suitability for efficient genotyping. Genotype analysis using CoAIMs along with a Bayesian clustering method (STRUCTURE) is able to discern continental origins including Europe/Middle East (Caucasians), East Asia, Africa, Native America, and Oceania. In addition to determining continental ancestry for individuals without significant admixture, we applied CoAIMs to ascertain admixture proportions of individuals of self declared race.
CoAIMs can be used to efficiently and effectively determine continental admixture proportions in a sample set. The CoAIMs panel is a valuable resource for genetic researchers performing case-control genetic association studies, as it can control for the confounding effects of population stratification. The MSAT-based approach used here has potential for broad applicability as a cost effective tool toward determining admixture proportions.
PMCID: PMC2955551  PMID: 20976178
13.  Genome-Wide Association Study reveals genetic risk underlying Parkinson’s disease 
Nature genetics  2009;41(12):1308-1312.
We performed a genome-wide association study (GWAS) in 1,713 Caucasian patients with Parkinson’s disease (PD) and 3,978 controls. After replication in 3,361 cases and 4,573 controls, two strong association signals were observed: in the α-synuclein gene(SNCA) (rs2736990, OR=1.23, p=2.24×10−16) and at the MAPT locus (rs393152, OR=0.77, p=1.95×10−16). We exchanged data with colleagues performing a GWAS in Asian PD cases. Association at SNCA was replicated in the Asian GWAS1, confirming this as a major risk locus across populations. We were able to replicate the effect of a novel locus detected in the Asian cohort (PARK16, rs823128, OR=0.66, p=7.29×10−8) and provide evidence supporting the role of common variability around LRRK2 in modulating risk for PD (rs1491923, OR=1.14, p=1.55×10−5). These data demonstrate an unequivocal role for common genetic variability in the etiology of typical PD and suggest population specific genetic heterogeneity in this disease.
PMCID: PMC2787725  PMID: 19915575
14.  Genetics in Clinical Trials 
PMCID: PMC2789592  PMID: 19478516
15.  Whole genome association studies of neuropsychiatric disease: An emerging era of collaborative genetic discovery 
Family history, which includes both common environmental and genetic effects, is associated with an increased risk for many neuropsychiatric diseases. Investigators have identified several disease-causing mutations for specific neuropsychiatric disorders that display Mendelian segregation. Such discoveries can lead to more rational drug design and improved intervention from a better understanding of the underlying biological mechanisms. However, a key challenge of genetic discovery in human complex diseases, including neuropsychiatric disorders, is that most diseases with genetic components display non-Mendelian patterns of inheritance. Recent advances in human population genetics include high-density genome-wide analyses of single nucleotide polymorphisms (SNPs) that make it possible to study complex genetic contributions to human disease. This approach is currently the most powerful strategy for analyzing the genetics of complex diseases. Genome-wide SNP analyses often require a large collaborative effort to collect, manage, and disseminate the numerous samples and corresponding clinical data. In this review we discuss the use of publicly available biorepositories for the collection and distribution of human genetic material, associated phenotypic information, and their use in genome-wide investigations of human neuropsychiatric diseases.
PMCID: PMC2656297  PMID: 19300590
repository; human; neurology; consent; genetics; bioinformatics
16.  Amyotrophic Lateral Sclerosis: An Emerging Era of Collaborative Gene Discovery 
PLoS ONE  2007;2(12):e1254.
Amyotrophic lateral sclerosis (ALS) is the most common form of motor neuron disease (MND). It is currently incurable and treatment is largely limited to supportive care. Family history is associated with an increased risk of ALS, and many Mendelian causes have been discovered. However, most forms of the disease are not obviously familial. Recent advances in human genetics have enabled genome-wide analyses of single nucleotide polymorphisms (SNPs) that make it possible to study complex genetic contributions to human disease. Genome-wide SNP analyses require a large sample size and thus depend upon collaborative efforts to collect and manage the biological samples and corresponding data. Public availability of biological samples (such as DNA), phenotypic and genotypic data further enhances research endeavors. Here we discuss a large collaboration among academic investigators, government, and non-government organizations which has created a public repository of human DNA, immortalized cell lines, and clinical data to further gene discovery in ALS. This resource currently maintains samples and associated phenotypic data from 2332 MND subjects and 4692 controls. This resource should facilitate genetic discoveries which we anticipate will ultimately provide a better understanding of the biological mechanisms of neurodegeneration in ALS.
PMCID: PMC2100166  PMID: 18060051

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