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The genetics of Parkinson disease.

ABSTRACT: Parkinson disease (PD) is the second most common neurodegenerative disorder. In most instances, PD is thought to result from a complex interaction between multiple genetic and environmental factors, though rare monogenic forms of the disease do exist. Mutations in 6 genes (SNCA, LRRK2, PRKN, DJ1, PINK1, and ATP13A2) have conclusively been shown to cause familial parkinsonism. In addition, common variation in 3 genes (MAPT, LRRK2, and SNCA) and loss-of-function mutations in GBA have been well-validated as susceptibility factors for PD. The function of these genes and their contribution to PD pathogenesis remain to be fully elucidated. The prevalence, incidence, clinical manifestations, and genetic components of PD are discussed in this review.

SUBMITTER: Bekris LM

PROVIDER: S-EPMC3044594 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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The genetics of Parkinson disease.

Bekris Lynn M LM Mata Ignacio F IF Zabetian Cyrus P CP

Journal of geriatric psychiatry and neurology 20101011 4

Parkinson disease (PD) is the second most common neurodegenerative disorder. In most instances, PD is thought to result from a complex interaction between multiple genetic and environmental factors, though rare monogenic forms of the disease do exist. Mutations in 6 genes (SNCA, LRRK2, PRKN, DJ1, PINK1, and ATP13A2) have conclusively been shown to cause familial parkinsonism. In addition, common variation in 3 genes (MAPT, LRRK2, and SNCA) and loss-of-function mutations in GBA have been well-val ...[more]

PMID: 20938043

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Project description:Parkinson's disease is a common, progressive neurodegenerative disorder, affecting 3% of those older than 75 years of age. Clinically, Parkinson's disease (PD) is associated with resting tremor, postural instability, rigidity, bradykinesia, and a good response to levodopa therapy. Over the last 15 years, numerous studies have confirmed that genetic factors contribute to the complex pathogenesis of PD. Highly penetrant mutations producing rare, monogenic forms of the disease have been discovered in singular genes such as SNCA, Parkin, DJ-1, PINK 1, LRRK2, and VPS35. Unique variants with incomplete penetrance in LRRK2 and GBA have been shown to be strong risk factors for PD in certain populations. Additionally, over 20 common variants with small effect sizes are now recognized to modulate the risk for PD. Investigating Mendelian forms of PD has provided precious insight into the pathophysiology that underlies the more common idiopathic form of disease; however, no treatment methodologies have developed. Furthermore, for identified common risk alleles, the functional basis underlying risk principally remains unknown. The challenge over the next decade will be to strengthen the findings delivered through genetic discovery by assessing the direct, biological consequences of risk variants in tandem with additional high-content, integrated datasets. This review discusses monogenic risk factors and mechanisms of Mendelian inheritance of Parkinson disease. Highly penetrant mutations in SNCA, Parkin, DJ-1, PINK 1, LRRK2 and VPS35 produce rare, monogenic forms of the disease, while unique variants within LRRK2 and GBA show incomplete penetrance and are strong risk factors for PD. Additionally, over 20 common variants with small effect sizes modulate disease risk. The challenge over the next decade is to strengthen genetic findings by assessing direct, biological consequences of risk variants in tandem with high-content, integrated datasets. This article is part of a special issue on Parkinson disease.

Project description:Background and objectivesNearly all genetic analyses of Parkinson disease (PD) have been in populations of European ancestry. We sought to test the ability of a machine learning method to extract accurate PD diagnoses from an electronic medical record (EMR) system, to see whether genetic variants identified in European populations generalize to individuals of African and Hispanic ancestries, and to compare the rates of PD across ancestries.MethodsA machine learning method using natural language processing was applied to EMRs of US veterans participating in the VA Million Veteran Program (MVP) to identify individuals with PD. These putative cases were vetted via blind chart review by a movement disorder specialist. A polygenic risk score (PRS) of 90 established genetic variants whose genotypes were imputed from a customized Axiom Biobank Array was evaluated in different case groups.ResultsThe EMR prediction scores had a distinct trimodal distribution, with 97% of the high group and only 30% of the middle group having a credible diagnosis of PD. Using the 3,542 cases from the high group matched 4:1 to controls, the PRS was highly predictive in individuals of European ancestry (n = 3,137 cases; OR = 1.82; p = 8.01E-48), and nearly identical effect sizes were seen in individuals of African (n = 184; OR = 2.07; p = 3.4E-4) and Hispanic ancestries (n = 221; OR = 2.13; p = 3.9E-6). The PRS was much less predictive for the 2,757 European ancestry cases who had an ICD code for PD but for whom the machine learning method had a lower confidence in their diagnosis. No novel ancestry-specific genetic variants were identified. Individuals with African ancestry had one-quarter the rate of PD compared with European or Hispanic ancestries aged 60-70 years and one half the rate in the 70-80 years age range. African American cases had a higher proportion of their DNA originating in Europe compared with African American controls.DiscussionMachine learning can reliably classify PD using data from a large EMR. Larger studies of non-European populations are required to confirm the generalizability of PD risk variants identified in populations of European ancestry and the increased risk coming from a higher proportion of European DNA in African Americans.

Project description:Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings.To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy.This observational study assessed individuals with PD who remotely completed 5 tasks (voice, finger tapping, gait, balance, and reaction time) on the smartphone application. We used a novel machine-learning-based approach to generate a mobile Parkinson disease score (mPDS) that objectively weighs features derived from each smartphone activity (eg, stride length from the gait activity) and is scaled from 0 to 100 (where higher scores indicate greater severity). Individuals with and without PD additionally completed standard in-person assessments of PD with smartphone assessments during a period of 6 months.Ability of the mPDS to detect intraday symptom fluctuations, the correlation between the mPDS and standard measures, and the ability of the mPDS to respond to dopaminergic medication.The mPDS was derived from 6148 smartphone activity assessments from 129 individuals (mean [SD] age, 58.7 [8.6] years; 56 [43.4%] women). Gait features contributed most to the total mPDS (33.4%). In addition, 23 individuals with PD (mean [SD] age, 64.6 [11.5] years; 11 [48%] women) and 17 without PD (mean [SD] age 54.2 [16.5] years; 12 [71%] women) completed in-clinic assessments. The mPDS detected symptom fluctuations with a mean (SD) intraday change of 13.9 (10.3) points on a scale of 0 to 100. The measure correlated well with the Movement Disorder Society Unified Parkinson Disease's Rating Scale total (r = 0.81; P < .001) and part III only (r = 0.88; P < .001), the Timed Up and Go assessment (r = 0.72; P = .002), and the Hoehn and Yahr stage (r = 0.91; P < .001). The mPDS improved by a mean (SD) of 16.3 (5.6) points in response to dopaminergic therapy.Using a novel machine-learning approach, we created and demonstrated construct validity of an objective PD severity score derived from smartphone assessments. This score complements standard PD measures by providing frequent, objective, real-world assessments that could enhance clinical care and evaluation of novel therapeutics.

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The genetics of Parkinson disease.

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The genetics of Parkinson disease.

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