We conducted a meta analysis of Parkinson’s disease genome-wide association studies using a common set of 7,893,274 variants across 13,708 cases and 95,282 controls. Twenty-six loci were identified as genome-wide significant; these and six additional previously reported loci were then tested in an independent set of 5,353 cases and 5,551 controls. Of the 32 tested SNPs, 24 replicated, including 6 novel loci. Conditional analyses within loci show four loci including GBA, GAK/DGKQ, SNCA, and HLA contain a secondary independent risk variant. In total we identified and replicated 28 independent risk variants for Parkinson disease across 24 loci. While the effect of each individual locus is small, a risk profile analysis revealed a substantial cummulative risk in a comparison highest versus lowest quintiles of genetic risk (OR=3.31, 95% CI: 2.55, 4.30; p-value = 2×10−16). We also show 6 risk loci associated with proximal gene expression or DNA methylation.

Background 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. Methods 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). Findings 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). Interpretation 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. Funding Please see the acknowledgements and funding section at the end of the manuscript.

Our objective was to design a genotyping platform that would allow rapid genetic characterization of samples in the context of genetic mutations and risk factors associated with common neurodegenerative diseases. The platform needed to be relatively affordable, rapid to deploy, and use a common and accessible technology. Central to this project, we wanted to make the content of the platform open to any investigator without restriction. In designing this array we prioritised a number of types of genetic variability for inclusion, such as known risk alleles, disease causing mutations, putative risk alleles, and other functionally important variants. The array was primarily designed to allow rapid screening of samples for disease causing mutations, and large population studies of risk factors. Notably, an explicit aim was to make this array widely available to facilitate data sharing across and within diseases. The resulting array, NeuroX, is a remarkably cost and time effective solution for high quality genotyping. NeuroX comprises a backbone of standard Illumina exome content of approximately 240,000 variants, and over 24,000 custom content variants focusing on neurological diseases. Data is generated at ~$50–$60 per sample using a 12-sample format chip and regular Infinium infrastructure; thus genotyping is rapid, and accessible to many investigators. Here, we describe the design of NeuroX, discuss the utility of NeuroX in the analyses of rare and common risk variants, and present quality control metrics and a brief primer for the analysis of NeuroX derived data.

• Genetically accurate xenografts of CMML are achievable with near 100% frequency in NSGS mice.• Robust human engraftment and overt phenotypes of CMML and JMML xenografts here facilitate preclinical therapeutic evaluation in vivo.Chronic myelomonocytic leukemia (CMML) and juvenile myelomonocytic leukemia (JMML) are myelodysplastic syndrome (MDS)/myeloproliferative neoplasm (MPN) overlap disorders characterized by monocytosis, myelodysplasia, and a characteristic hypersensitivity to granulocyte-macrophage colony-stimulating factor (GM-CSF). Currently, there are no available disease-modifying therapies for CMML, nor are there preclinical models that fully recapitulate the unique features of CMML. Through use of immunocompromised mice with transgenic expression of human GM-CSF, interleukin-3, and stem cell factor in a NOD/SCID-IL2Rg null background (NSGS mice), we demonstrate remarkable engraftment of CMML and JMML providing the first examples of serially transplantable and genetically accurate models of CMML. Xenotransplantation of CD34 1 cells (n 5 8 patients) or unfractionated bone marrow (BM) or peripheral blood mononuclear cells (n 5 10) resulted in robust engraftment of CMML in BM, spleen, liver, and lung of recipients (n 5 82 total mice). Engrafted cells were myeloid-restricted and matched the immunophenotype, morphology, and genetic mutations of the corresponding patient. Similar levels of engraftment were seen upon serial transplantation of human CD34 1 cells in secondary NSGS recipients (2/5 patients, 6/11 mice), demonstrating the durability of CMML grafts and functionally validating CD34 1 cells as harboring the disease-initiating compartment in vivo.Successful engraftments of JMML primary samples were also achieved in all NSGS recipients (n 5 4 patients, n 5 12 mice). Engraftment of CMML and JMML resulted in overt phenotypic abnormalities and lethality in recipients, which facilitated evaluation of the JAK2/FLT3 inhibitor pacritinib in vivo. These data reveal that NSGS mice support the development of CMML and JMML disease-initiating and mature leukemic cells in vivo, allowing creation of genetically accurate preclinical models of these disorders. (Blood. 2017;130(4):397-407)