Generating Molecular Fragmentation Graphs with Autoregressive Neural Networks.

The accurate prediction of tandem mass spectra from molecular structures has the potential to unlock new metabolomic discoveries by augmenting the community's libraries of experimental reference standards. Cheminformatic spectrum prediction strategies use a "bond-breaking" framework to iteratively simulate mass spectrum fragmentations, but these methods are (a) slow due to the need to exhaustively and combinatorially break molecules and (b) inaccurate as they often rely upon heuristics to predict the intensity of each resulting fragment; neural network alternatives mitigate computational cost but are black-box and not inherently more accurate. We introduce a physically grounded neural approach that learns to predict each breakage event and score the most relevant subset of molecular fragments quickly and accurately. We evaluate our model by predicting spectra from both public and private standard libraries, demonstrating that our hybrid approach offers state-of-the-art prediction accuracy, improved metabolite identification from a database of candidates, and higher interpretability when compared to previous breakage methods and black-box neural networks. The grounding of our approach in physical fragmentation events shows especially great promise for elucidating natural product molecules with more complex scaffolds.

Dry-Cleaning Chemicals and a Cluster of Parkinson's Disease and Cancer: A Retrospective Investigation.

BACKGROUND: Environmental exposure to trichloroethylene (TCE), a carcinogenic dry-cleaning chemical, may be linked to Parkinson's disease (PD). OBJECTIVE: The objective of this study was to determine whether PD and cancer were elevated among attorneys who worked near a contaminated site. METHODS: We surveyed and evaluated attorneys with possible exposure and assessed a comparison group. RESULTS: Seventy-nine of 82 attorneys (96.3%; mean [SD] age: 69.5 [11.4] years; 89.9% men) completed at least one phase of the study. For comparison, 75 lawyers (64.9 [10.2] years; 65.3% men) underwent clinical evaluations. Four (5.1%) of them who worked near the polluted site reported PD, more than expected based on age and sex (1.7%; P = 0.01) but not significantly higher than the comparison group (n = 1 [1.3%]; P = 0.37). Fifteen (19.0%), compared to four in the comparison group (5.3%; P = 0.049), had a TCE-related cancer. CONCLUSIONS: In a retrospective study, diagnoses of PD and TCE-related cancers appeared to be elevated among attorneys who worked next to a contaminated dry-cleaning site. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Resonant learning in scale-free networks.

Large networks of interconnected components, such as genes or machines, can coordinate complex behavioral dynamics. One outstanding question has been to identify the design principles that allow such networks to learn new behaviors. Here, we use Boolean networks as prototypes to demonstrate how periodic activation of network hubs provides a network-level advantage in evolutionary learning. Surprisingly, we find that a network can simultaneously learn distinct target functions upon distinct hub oscillations. We term this emergent property resonant learning, as the new selected dynamical behaviors depend on the choice of the period of the hub oscillations. Furthermore, this procedure accelerates the learning of new behaviors by an order of magnitude faster than without oscillations. While it is well-established that modular network architecture can be selected through evolutionary learning to produce different network behaviors, forced hub oscillations emerge as an alternative evolutionary learning strategy for which network modularity is not necessarily required.

MIST-CF: Chemical Formula Inference from Tandem Mass Spectra.

Chemical formula annotation for tandem mass spectrometry (MS/MS) data is the first step toward structurally elucidating unknown metabolites. While great strides have been made toward solving this problem, the current state-of-the-art method depends on time-intensive, proprietary, and expert-parametrized fragmentation tree construction and scoring. In this work, we extend our previous spectrum Transformer methodology into an energy-based modeling framework, MIST-CF: Metabolite Inference with Spectrum Transformers for Chemical Formula prediction, for learning to rank chemical formula and adduct assignments given an unannotated MS/MS spectrum. Importantly, MIST-CF learns in a data-dependent fashion using a Formula Transformer neural network architecture and circumvents the need for fragmentation tree construction. We train and evaluate our model on a large open-access database, showing an absolute improvement of 10% top 1 accuracy over other neural network architectures. We further validate our approach on the CASMI2022 challenge data set, achieving nearly equivalent performance to the winning entry within the positive mode category without any manual curation or postprocessing of our results. These results demonstrate an exciting strategy to more powerfully leverage MS2 fragment peaks for predicting MS1 precursor chemical formulas with data-driven learning.

Machine learning modeling of family wide enzyme-substrate specificity screens.

Biocatalysis is a promising approach to sustainably synthesize pharmaceuticals, complex natural products, and commodity chemicals at scale. However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their natural chemical transformation on non-natural substrates. While machine learning and in silico directed evolution are well-posed for this predictive modeling challenge, efforts to date have primarily aimed to increase activity against a single known substrate, rather than to identify enzymes capable of acting on new substrates of interest. To address this need, we curate 6 different high-quality enzyme family screens from the literature that each measure multiple enzymes against multiple substrates. We compare machine learning-based compound-protein interaction (CPI) modeling approaches from the literature used for predicting drug-target interactions. Surprisingly, comparing these interaction-based models against collections of independent (single task) enzyme-only or substrate-only models reveals that current CPI approaches are incapable of learning interactions between compounds and proteins in the current family level data regime. We further validate this observation by demonstrating that our no-interaction baseline can outperform CPI-based models from the literature used to guide the discovery of kinase inhibitors. Given the high performance of non-interaction based models, we introduce a new structure-based strategy for pooling residue representations across a protein sequence. Altogether, this work motivates a principled path forward in order to build and evaluate meaningful predictive models for biocatalysis and other drug discovery applications.

EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates.

Data-driven computer-aided synthesis planning utilizing organic or biocatalyzed reactions from large databases has gained increasing interest in the last decade, sparking the development of numerous tools to extract, apply, and score general reaction templates. The generation of reaction rules for enzymatic reactions is especially challenging since substrate promiscuity varies between enzymes, causing the optimal levels of rule specificity and optimal number of included atoms to differ between enzymes. This complicates an automated extraction from databases and has promoted the creation of manually curated reaction rule sets. Here, we present EHreact, a purely data-driven open-source software tool, to extract and score reaction rules from sets of reactions known to be catalyzed by an enzyme at appropriate levels of specificity without expert knowledge. EHreact extracts and groups reaction rules into tree-like structures, Hasse diagrams, based on common substructures in the imaginary transition structures. Each diagram can be utilized to output a single or a set of reaction rules, as well as calculate the probability of a new substrate to be processed by the given enzyme by inferring information about the reactive site of the enzyme from the known reactions and their grouping in the template tree. EHreact heuristically predicts the activity of a given enzyme on a new substrate, outperforming current approaches in accuracy and functionality.

Short-term deceleration capacity of heart rate: a sensitive marker of cardiac autonomic dysfunction in idiopathic Parkinson's disease.

PURPOSE: Cardiac autonomic dysfunction in idiopathic Parkinson's disease (PD) manifests as reduced heart rate variability (HRV). In the present study, we explored the deceleration capacity of heart rate (DC) in patients with idiopathic PD, an advanced HRV marker that has proven clinical utility. METHODS: Standard and advanced HRV measures derived from 7-min electrocardiograms in 20 idiopathic PD patients and 27 healthy controls were analyzed. HRV measures were compared using regression analysis, controlling for age, sex, and mean heart rate. RESULTS: Significantly reduced HRV was found only in the subcohort of PD patients older than 60 years. Low- frequency power and global HRV measures were lower in patients than in controls, but standard beat-to-beat HRV markers (i.e., rMSSD and high-frequency power) were not significantly different between groups. DC was significantly reduced in the subcohort of PD patients older than 60 years compared to controls. CONCLUSIONS: Deceleration-related oscillations of HRV were significantly reduced in the older PD patients compared to healthy controls, suggesting that short-term DC may be a sensitive marker of cardiac autonomic dysfunction in PD. DC may be complementary to traditional markers of short-term HRV for the evaluation of autonomic modulation in PD. Further study to examine the association between DC and cardiac adverse events in PD is needed to clarify the clinical relevance of DC in this population.

Concordance for Parkinson's disease in twins: A 20-year update.

During the 1990s, we estimated the genetic contribution to Parkinson's disease risk in a large, population-based twin registry. Because many unaffected twins were still alive, previous concordance estimates were based on incomplete information. Ninety-five percent of twins are now deceased. Here, we update concordance and heritability through 2015 using National Death Index data. In total, we identified 30 concordant and 193 discordant pairs. Proband-wise concordance was 0.20 in monozygotic and 0.13 in dizygotic pairs. Heritability was 0.27 overall, 0.83 in pairs diagnosed ≤50, and 0.19 in pairs diagnosed >50. High concordance in dizygotic twins suggests shared effects of early childhood environment. Ann Neurol 2019;85:600-605.

Nonsteroidal Anti-inflammatory Use and LRRK2 Parkinson's Disease Penetrance.

BACKGROUND: The penetrance of leucine rich repeat kinase 2 (LRRK2) mutations is incomplete and may be influenced by environmental and/or other genetic factors. Nonsteroidal anti-inflammatory drugs (NSAIDs) are known to reduce inflammation and may lower Parkinson's disease (PD) risk, but their role in LRRK2-associated PD is unknown. OBJECTIVES: The objective of this study is to evaluate the association of regular NSAID use and LRRK2-associated PD. METHODS: Symptomatic ("LRRK2-PD") and asymptomatic ("LRRK2-non-PD") participants with LRRK2 G2019S, R1441X, or I2020T variants (definitely pathogenic variant carriers) or G2385R or R1628P variants (risk variant carriers) from 2 international cohorts provided information on regular ibuprofen and/or aspirin use (≥2 pills/week for ≥6 months) prior to the index date (diagnosis date for PD, interview date for non-PD). Multivariate logistic regression was used to evaluate the relationship between regular NSAID use and PD for any NSAID, separately for ibuprofen and aspirin in all carriers and separately in pathogenic and risk variant groups. RESULTS: A total of 259 LRRK2-PD and 318 LRRK2-non-PD participants were enrolled. Regular NSAID use was associated with reduced odds of PD in the overall cohort (odds ratio [OR], 0.34; 95% confidence interval [CI], 0.21-0.57) and in both pathogenic and risk variant carriers (ORPathogenic , 0.38; 95% CI, 0.21-0.67 and ORRiskVariant , 0.19; 95% CI, 0.04-0.99). Similar associations were observed for ibuprofen and aspirin separately (ORIbuprofen , 0.19; 95% CI, 0.07-0.50 and ORAspirin , 0.51; 95% CI, 0.28-0.91). CONCLUSIONS: Regular NSAID use may be associated with reduced penetrance in LRRK2-associated PD. The LRRK2 protein is involved in inflammatory pathways and appears to be modulated by regular anti-inflammatory use. Longitudinal observational and interventional studies of NSAID exposure and LRRK2-PD are needed to confirm this association. © 2020 International Parkinson and Movement Disorder Society.

Cervical dystonia incidence and diagnostic delay in a multiethnic population.

BACKGROUND: Current cervical dystonia (CD) incidence estimates are based on small numbers in relatively ethnically homogenous populations. The frequency and consequences of delayed CD diagnosis is poorly characterized. OBJECTIVES: To determine CD incidence and characterize CD diagnostic delay within a large, multiethnic integrated health maintenance organization. METHODS: We identified incident CD cases using electronic medical records and multistage screening of more than 3 million Kaiser Permanente Northern California members from January 1, 2003, to December 31, 2007. A final diagnosis was made by movement disorders specialist consensus. Diagnostic delay was measured by questionnaire and health utilization data. Incidence rates were estimated assuming a Poisson distribution of cases and directly standardized to the 2000 U.S. census. Multivariate logistic regression models were employed to assess diagnoses and behaviors preceding CD compared with matched controls, adjusting for age, sex, and membership duration. RESULTS: CD incidence was 1.18/100,000 person-years (95% confidence interval [CI], 0.35-2.0; women, 1.81; men, 0.52) based on 200 cases over 15.4 million person-years. Incidence increased with age. Half of the CD patients interviewed reported diagnostic delay. Diagnoses more common in CD patients before the index date included essential tremor (odds ratio [OR] 68.1; 95% CI, 28.2-164.5), cervical disc disease (OR 3.83; 95% CI, 2.8-5.2), neck sprain/strain (OR 2.77; 95% CI, 1.99-3.62), anxiety (OR 2.24; 95% CI, 1.63-3.11) and depression (OR 1.94; 95% CI, 1.4-2.68). CONCLUSIONS: CD incidence is greater in women and increases with age. Diagnostic delay is common and associated with adverse effects. © 2019 International Parkinson and Movement Disorder Society.

Gradient boosting for Parkinson's disease diagnosis from voice recordings.

BACKGROUND: Parkinson's Disease (PD) is a clinically diagnosed neurodegenerative disorder that affects both motor and non-motor neural circuits. Speech deterioration (hypokinetic dysarthria) is a common symptom, which often presents early in the disease course. Machine learning can help movement disorders specialists improve their diagnostic accuracy using non-invasive and inexpensive voice recordings. METHOD: We used "Parkinson Dataset with Replicated Acoustic Features Data Set" from the UCI-Machine Learning repository. The dataset included 44 speech-test based acoustic features from patients with PD and controls. We analyzed the data using various machine learning algorithms including Light and Extreme Gradient Boosting, Random Forest, Support Vector Machines, K-nearest neighborhood, Least Absolute Shrinkage and Selection Operator Regression, as well as logistic regression. We also implemented a variable importance analysis to identify important variables classifying patients with PD. RESULTS: The cohort included a total of 80 subjects: 40 patients with PD (55% men) and 40 controls (67.5% men). Disease duration was 5 years or less for all subjects, with a mean Unified Parkinson's Disease Rating Scale (UPDRS) score of 19.6 (SD 8.1), and none were taking PD medication. The mean age for PD subjects and controls was 69.6 (SD 7.8) and 66.4 (SD 8.4), respectively. Our best-performing model used Light Gradient Boosting to provide an AUC of 0.951 with 95% confidence interval 0.946-0.955 in 4-fold cross validation using only seven acoustic features. CONCLUSIONS: Machine learning can accurately detect Parkinson's disease using an inexpensive and non-invasive voice recording. Light Gradient Boosting outperformed other machine learning algorithms. Such approaches could be used to inexpensively screen large patient populations for Parkinson's disease.

Environment, lifestyle, and Parkinson's disease: Implications for prevention in the next decade.

There is evidence from observational studies for a role of a number of environmental exposures and lifestyle habits in modulating the risk for Parkinson's disease. Environmental and lifestyle associations, if causal, represent opportunities for Parkinson's disease prevention or disease modification at individual and population levels. In the past decade, additional evidence has been published that improves causal inference and/or enhances our understanding of the complexity of these associations. A number of gene-environment interactions have been elucidated, and our understanding of the roles of physical activity, pesticide and other chemical exposures, dietary habits, emotional stress, head injury, and smoking has been refined. In the next decade, better techniques will help us to close the gaps in our knowledge, including taking into account Parkinson's disease heterogeneity and gene and risk factor interactions in observational studies. To do this, larger datasets, global consortia, genomewide environment interaction studies, prospective studies throughout the lifespan, and improvements in the methodology of clinical trials of physical activity will be key. Despite the caveats of observational studies, a number of low-risk and potentially high-yield recommendations for lifestyle modification could be made to minimize the individual and societal burdens of Parkinson's disease, including dietary modifications, increasing physical activity, and head injury avoidance. Furthermore, a reduction in pesticide use could have a major impact on global health related to and beyond Parkinson's disease. Given the increasing prevalence of this disorder, formulating and promoting these recommendations should be a high priority. © 2019 International Parkinson and Movement Disorder Society.

Increased markers of cardiac vagal activity in leucine-rich repeat kinase 2-associated Parkinson's disease.

PURPOSE: Cardiac autonomic dysfunction manifests as reduced heart rate variability (HRV) in idiopathic Parkinson's disease (PD), but no significant reduction has been found in PD patients who carry the LRRK2 mutation. Novel HRV features have not been investigated in these individuals. We aimed to assess cardiac autonomic modulation through standard and novel approaches to HRV analysis in individuals who carry the LRRK2 G2019S mutation. METHODS: Short-term electrocardiograms were recorded in 14 LRRK2-associated PD patients, 25 LRRK2-non-manifesting carriers, 32 related non-carriers, 20 idiopathic PD patients, and 27 healthy controls. HRV measures were compared using regression modeling, controlling for age, sex, mean heart rate, and disease duration. Discriminant analysis highlighted the feature combination that best distinguished LRRK2-associated PD from controls. RESULTS: Beat-to-beat and global HRV measures were significantly increased in LRRK2-associated PD patients compared with controls (e.g., deceleration capacity of heart rate: p = 0.006) and idiopathic PD patients (e.g., 8th standardized moment of the interbeat interval distribution: p = 0.0003), respectively. LRRK2-associated PD patients also showed significantly increased irregularity of heart rate dynamics, as quantified by Rényi entropy, when compared with controls (p = 0.002) and idiopathic PD patients (p = 0.0004). Ordinal pattern statistics permitted the identification of LRRK2-associated PD individuals with 93% sensitivity and 93% specificity. Consistent results were found in a subgroup of LRRK2-non-manifesting carriers when compared with controls. CONCLUSIONS: Increased beat-to-beat HRV in LRRK2 G2019S mutation carriers compared with controls and idiopathic PD patients may indicate augmented cardiac autonomic cholinergic activity, suggesting early impairment of central vagal feedback loops in LRRK2-associated PD.

The NAS-NRC Twin Registry and Duke Twins Study of Memory in Aging: An Update.

The National Academy of Sciences-National Research Council (NAS-NRC) Twin Registry is one of the oldest, national population-based twin registries in the USA. It comprises 15,924 White male twin pairs born in the years 1917-1927 (N = 31.848), both of whom served in the armed forces, chiefly during World War II. This article updates activities in this registry since the most recent report in Twin Research and Human Genetics (Page, 2006). Records-based data include information from enlistment charts and Veterans Administration data linkages. There have been three major epidemiologic questionnaires and an education and earnings survey. Separate data collection efforts with the NAS-NRC registry include the National Heart, Lung, and Blood Institute (NHLBI) subsample, the Duke Twins Study of Memory in Aging and a clinically based study of Parkinson's disease. Progress has been made on consolidating the various data holdings of the NAS-NRC Twin Registry. Data that had been available through the National Academy of Sciences are now freely available through National Archive of Computerized Data on Aging (NACDA).

Environmental toxins and Parkinson's disease.

Parkinson's disease (PD) is a chronic, progressive, disabling neurodegenerative disorder that begins in mid to late life and is characterized by motor impairment, autonomic dysfunction, and, in many, psychological and cognitive changes. Recent advances have helped delineate pathogenetic mechanisms, yet the cause of PD in most individuals is unknown. Although at least 15 genes and genetic loci have been associated with PD, identified genetic causes are responsible for only a few percent of cases. Epidemiologic studies have found increased risk of PD associated with exposure to environmental toxicants such as pesticides, solvents, metals, and other pollutants, and many of these compounds recapitulate PD pathology in animal models. This review summarizes the environmental toxicology of PD, highlighting the consistency of observations across cellular, animal, and human studies of PD pathogenesis.

Heart rate variability in leucine-rich repeat kinase 2-associated Parkinson's disease.

BACKGROUND: Heart rate variability is reduced in idiopathic PD, indicating cardiac autonomic dysfunction likely resulting from peripheral autonomic synucleinopathy. Little is known about heart rate variability in leucine-rich repeat kinase 2-associated PD. OBJECTIVES: This study investigated heart rate variability in LRRK2-associated PD. METHODS: Resting electrocardiograms were obtained from 20 individuals with LRRK2-associated PD, 37 nonmanifesting carriers, 48 related noncarriers, 26 idiopathic PD patients, and 32 controls. Linear regression modelling compared time and frequency domain values, adjusting for age, sex, heart rate, and disease duration. RESULTS: Low-frequency power and the ratio of low-high frequency power were reduced in idiopathic PD versus controls (P < .008, P < .029 respectively). In contrast, individuals with LRRK2-associated PD were not statistically different from controls in any parameter measured. Furthermore, all parameters trended toward being higher in LRRK2-associated PD when compared with idiopathic PD. CONCLUSIONS: Heart rate variability may remain intact in LRRK2-associated PD, adding to a growing literature supporting clinical-pathologic differences between LRRK2-associated and idiopathic PD. © 2017 International Parkinson and Movement Disorder Society.

"Supersaturated" self-assembled charge-selective interfacial layers for organic solar cells.

To achieve densely packed charge-selective organosilane-based interfacial layers (IFLs) on the tin-doped indium oxide (ITO) anodes of organic photovoltaic (OPV) cells, a series of Ar2N-(CH2)n-SiCl3 precursors with Ar = 3,4-difluorophenyl, n = 3, 6, 10, and 18, was synthesized, characterized, and chemisorbed on OPV anodes to serve as IFLs. To minimize lateral nonbonded -NAr2···Ar2N- repulsions which likely limit IFL packing densities in the resulting self-assembled monolayers (SAMs), precursor mixtures having both small and large n values are simultaneously deposited. These "heterogeneous" SAMs are characterized by a battery of techniques: contact angle measurements, X-ray reflectivity, X-ray photoelectron spectroscopy, ultraviolet photoelectron spectroscopy (UPS), cyclic voltammetry, and DFT computation. It is found that the headgroup densities of these "supersaturated" heterogeneous SAMs (SHSAMs) are enhanced by as much as 17% versus their homogeneous counterparts. Supersaturation significantly modifies the IFL properties including the work function (as much as 16%) and areal dipole moment (as much as 49%). Bulk-heterojunction OPV devices are fabricated with these SHSAMs: ITO/IFL/poly[[4,8-bis[(2-ethylhexyl)oxy]benzo[1,2-b:4,5-b']dithiophene-2,6-diyl][2-[[(2-ethylhexyl)oxy]carbonyl]-3-fluorothieno[3,4-b]thiophenediyl]]:phenyl-C71-butyric acid methyl ester (PTB7:PC71BM)/LiF/Al. OPVs having SHSAM IFLs exhibit significantly enhanced performance (PCE by 54%; Voc by 35%) due to enhanced charge selectivity and collection, with the PCE rivaling or exceeding that of PEDOT:PSS IFL devices -7.62%. The mechanism underlying the enhanced performance involves modified hole collection and selectivity efficiency inferred from the UPS data. The ITO/SAM/SHSAM surface potential imposed by the dipolar SAMs causes band bending and favorably alters the Schottky barrier height. Thus, interfacial charge selectivity and collection are enhanced as evident in the greater OPV Voc.

Peptidoglycan recognition protein genes and risk of Parkinson's disease.

Increased gut permeability, inflammation, and colonic α-synuclein pathology are present in early Parkinson's disease (PD) and have been proposed to contribute to PD pathogenesis. Peptidoglycan is a structural component of the bacterial cell wall. Peptidoglycan recognition proteins (PGRPs) maintain healthy gut microbial flora by regulating the immune response to both commensal and harmful bacteria. We tested the hypothesis that variants in genes that encode PGRPs are associated with PD risk. Participants in two independent case-control studies were genotyped for 30 single-nucleotide polymorphisms (SNPs) in the four PGLYRP genes. Using logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CI) adjusted for potential confounding variables, we conducted analyses in each study, separately and pooled. One SNP failed the assay, and three had little to no variation. The ORs were similar in both study populations. In pooled analyses, three of seven PGLYRP2 SNPs (rs3813135, rs733731, rs892145), one of five PGLYRP3 SNPs (rs2987763), and six of nine PGLYRP4 SNPs (rs10888557, rs12063091, rs3006440, rs3006448, rs3006458, and rs3014864) were significantly associated with PD risk. Association was strongest for PGLYRP4 5'untranslated region (UTR) SNP rs10888557 (GG reference, CG OR 0.6 [95%CI 0.4-0.9], CC OR 0.15 [95%CI 0.04-0.6]; log-additive P-trend, 0.0004). Common variants in PGLYRP genes are associated with PD risk in two independent studies. These results require replication, but they are consistent with hypotheses of a causative role for the gut microbiota and gastrointestinal immune response in PD.

Web-based genome-wide association study identifies two novel loci and a substantial genetic component for Parkinson's disease.

Although the causes of Parkinson's disease (PD) are thought to be primarily environmental, recent studies suggest that a number of genes influence susceptibility. Using targeted case recruitment and online survey instruments, we conducted the largest case-control genome-wide association study (GWAS) of PD based on a single collection of individuals to date (3,426 cases and 29,624 controls). We discovered two novel, genome-wide significant associations with PD-rs6812193 near SCARB2 (p = 7.6 × 10(-10), OR = 0.84) and rs11868035 near SREBF1/RAI1 (p = 5.6 × 10(-8), OR = 0.85)-both replicated in an independent cohort. We also replicated 20 previously discovered genetic associations (including LRRK2, GBA, SNCA, MAPT, GAK, and the HLA region), providing support for our novel study design. Relying on a recently proposed method based on genome-wide sharing estimates between distantly related individuals, we estimated the heritability of PD to be at least 0.27. Finally, using sparse regression techniques, we constructed predictive models that account for 6%-7% of the total variance in liability and that suggest the presence of true associations just beyond genome-wide significance, as confirmed through both internal and external cross-validation. These results indicate a substantial, but by no means total, contribution of genetics underlying susceptibility to both early-onset and late-onset PD, suggesting that, despite the novel associations discovered here and elsewhere, the majority of the genetic component for Parkinson's disease remains to be discovered.

The disease intersection of susceptibility and exposure: chemical exposures and neurodegenerative disease risk.

Alzheimer's disease, Parkinson's disease, and motor neuron disease, the most common of the late-life neurodegenerative disorders, are in most cases thought to have complex etiologies. Common features among these disorders include insidious onset, pathological findings of protein aggregates and selected neuronal degeneration, and resulting characteristic clinical syndromes. The number of elders in the United States, including aging veterans, is increasing. Investigation of causes and preventive interventions for neurodegenerative disorders is increasingly relevant. Recent epidemiological and laboratory studies suggest that exposures years or decades before diagnosis can trigger the processes that ultimately result in a neurodegenerative disease. If this is correct, preventive measures may be needed in midlife or earlier. This article will focus on putative risk factors relevant to military service.