Katrina Gwinn

Genome-wide association study reveals genetic risk underlying Parkinson's disease

We performed a genome-wide association study (GWAS) in 1,713 individuals of European ancestry with Parkinsons disease (PD) and 3,978 controls. After replication in 3,361 cases and 4,573 controls, we observed two strong association signals, one in the gene encoding α-synuclein (SNCA; rs2736990, OR = 1.23, P = 2.24 × 10−16) and another at the MAPT locus (rs393152, OR = 0.77, P = 1.95 × 10−16). We exchanged data with colleagues performing a GWAS in Japanese PD cases. Association to PD at SNCA was replicated in the Japanese GWAS, confirming this as a major risk locus across populations. We replicated the effect of a new locus detected in the Japanese cohort (PARK16, rs823128, OR = 0.66, P = 7.29 × 10−8) and provide supporting evidence that common variation around LRRK2 modulates risk for PD (rs1491923, OR = 1.14, P = 1.55 × 10−5). These data demonstrate an unequivocal role for common genetic variants in the etiology of typical PD and suggest population-specific genetic heterogeneity in this disease.

Genomic investigation of alpha-synuclein multiplication and parkinsonism.

Copy number variation is a common polymorphic phenomenon within the human genome. Although 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).

Creation of an Open-Access, Mutation-Defined Fibroblast Resource for Neurological Disease Research

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.

Parkinson's disease and α‐synuclein expression

Genetic studies of Parkinsons disease over the last decade or more have revolutionized our understanding of this condition. α‐Synuclein was the first gene to be linked to Parkinsons 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 Parkinsons disease pathogenesis. Finally, we discuss potential strategies for disease‐modifying therapies for this currently incurable disorder.

Common Data Elements for Traumatic Brain Injury: Recommendations From the Biospecimens and Biomarkers Working Group

Recent advances in genomics, proteomics, and biotechnology have provided unprecedented opportunities for translational research and personalized medicine. Human biospecimens and biofluids represent an important resource from which molecular data can be generated to detect and classify injury and to identify molecular mechanisms and therapeutic targets. To date, there has been considerable variability in biospecimen and biofluid collection, storage, and processing in traumatic brain injury (TBI) studies. To realize the full potential of this important resource, standardization and adoption of best practice guidelines are required to insure the quality and consistency of these specimens. The aim of the Biospecimens and Biomarkers Working Group was to provide recommendations for core data elements for TBI research and develop best practice guidelines to standardize the quality and accessibility of these specimens. Consensus recommendations were developed through interactions with focus groups and input from stakeholders participating in the interagency workshop on Standardization of Data Collection in TBI and Psychological Health held in Washington, DC, in March 2009. With the adoption of these standards and best practices, future investigators will be able to obtain data across multiple studies with reduced costs and effort and accelerate the progress of genomic, proteomic, and metabolomic research in TBI.

Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study

BACKGROUND Accurate diagnosis and early detection of complex diseases, such as Parkinsons disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinsons disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts. METHODS We developed a model for disease classification using data from the Parkinsons Progression Marker Initiative (PPMI) study for 367 patients with Parkinsons disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinsons disease, age, and gender were algorithmically selected by stepwise logistic regression as significant contributors to our classifying model. We then tested the model with data from 825 patients with Parkinsons disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinsons Disease Biomarkers Program (PDBP), the Parkinsons Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinsons Disease Research Center of Excellence cohort (Penn-Udall). Additionally, we used our model to investigate patients who had imaging scans without evidence of dopaminergic deficit (SWEDD). FINDINGS In the population from PPMI, our initial model correctly distinguished patients with Parkinsons 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) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinsons disease, with AUCs of 0·894 (95% CI 0·867-0·921) in the PDBP cohort, 0·998 (0·992-1·000) in PARS, 0·955 (no 95% CI available) in 23andMe, 0·929 (0·896-0·962) in LABS-PD, and 0·939 (0·891-0·986) in the Penn-Udall cohort. Four of 17 SWEDD participants who our model classified as having Parkinsons disease converted to Parkinsons disease within 1 year, whereas only one of 38 SWEDD participants who were not classified as having Parkinsons disease underwent conversion (test of proportions, p=0·003). INTERPRETATION Our model provides a potential new approach to distinguish participants with Parkinsons disease from controls. If the model can also identify individuals with prodromal or preclinical Parkinsons disease in prospective cohorts, it could facilitate identification of biomarkers and interventions. FUNDING National Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J Fox Foundation.

Characterization of DCTN1 genetic variability in neurodegeneration

Objective: Recently, mutations in DCTN1 were found to cause Perry syndrome, a parkinsonian disorder with TDP-43-positive pathology. Previously, mutations in DCTN1 were identified in a family with lower motor neuron disease, in amyotrophic lateral sclerosis (ALS), and in a family with ALS/frontotemporal dementia (FTD), suggesting a central role for DCTN1 in neurodegeneration. Methods: In this study we sequenced all DCTN1 exons and exon-intron boundaries in 286 samples diagnosed with Parkinson disease (PD), frontotemporal lobar degeneration (FTLD), or ALS. Results: This analysis revealed 36 novel variants (9 missense, 5 silent, and 22 noncoding). Segregation analysis in families and association studies in PD, FTLD, and ALS case–control series did not identify any variants segregating with disease or associated with increased disease risk. Conclusions: This study suggests that pathogenic mutations in DCTN1 are rare and do not play a common role in the development of Parkinson disease, frontotemporal lobar degeneration, or amyotrophic lateral sclerosis.

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 National Institute of Neurological Disorders and Stroke SiGN (Stroke Genetics Network) contributes substantially to meta-analyses that focus on specific subtypes of stroke. Methods— The National Institute of Neurological Disorders and Stroke SiGN includes ischemic stroke cases from 24 genetic research centers: 13 from the United States and 11 from Europe. Investigators harmonize ischemic stroke phenotyping using the Web-based causative classification of stroke system, with data entered by trained and certified adjudicators at participating genetic research centers. Through the Center for Inherited Diseases Research, the Network plans to genotype 10 296 carefully phenotyped stroke cases using genome-wide single nucleotide polymorphism arrays and adds to these another 4253 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. Center for Inherited Diseases Research–generated genotypes and corresponding phenotypes will be shared with the scientific community through the US National Center for Biotechnology Information database of Genotypes and Phenotypes, and brain MRI studies will be centrally archived. Conclusions— The Stroke Genetics Network, 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.

Clinical Features, with Video Documentation, of the original Familial Lewy Body Parkinsonism caused by α-Synuclein Triplication (Iowa Kindred)

This family has autosomal dominant parkinsonism due to α-synuclein triplication. Dopamine-responsive parkinsonism, cognitive difficulties, RBD, and dysautonomia are common. End-stage illness includes contractures, myoclonus, and severe motor and cognitive impairment. Of note, there was pre-symptomatic amphetamine/cocaine use in two of the cases described.

Agreement between TOAST and CCS ischemic stroke classification: The NINDS SiGN Study

Objective: 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. Methods: 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. Results: 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). Conclusion: 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.