Background Genome-wide association studies (GWASs) in Parkinson's disease (PD) have increased the scope of biological knowledge about the disease over the past decade. We sought to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into disease etiology. Methods We performed the largest meta-GWAS of PD to date, involving the analysis of 7.8M SNPs in 37.7K cases, 18.6K UK Biobank proxy-cases (having a first degree relative with PD), and 1.4M controls. We carried out a meta-analysis of this GWAS data to nominate novel loci. We then evaluated heritable risk estimates and predictive models using this data. We also utilized large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type and biological pathway enrichments for the identified risk factors. Additionally we examined shared genetic risk between PD and other phenotypes of interest via genetic correlations followed by Mendelian randomization. Findings We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16-36% of the heritable risk of PD depending on prevalence. Integrating methylation and expression data within a Mendelian randomization framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested PD loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes, smoking status, and educational attainment. Mendelian randomization between cognitive performance and PD risk showed a robust association. Interpretation These data provide the most comprehensive understanding of the genetic architecture of PD to date by revealing many additional PD risk loci, providing a biological context for these risk factors, and demonstrating that a considerable genetic component of this disease remains unidentified. Funding See supplemental materials (Text S2). lead to earlier detection and refined diagnostics, which may help improve clinical trials (4). The generation of copious amounts of public summary statistics created by this effort relating to both the GWAS and subsequent analyses of gene expression and methylation patterns may be of use to investigators planning follow-up functional studies in stem cells or other cellular screens, allowing them to prioritize targets more efficiently using our data as additional evidence. We hope our findings may have some downstream clinical impact in the future such as improved patient stratification for clinical trials and genetically informed drug targets.

Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research.

We performed the largest genome-wide association study of PD to date, involving the analysis of 7.8M SNPs in 37.7K cases, 18.6K UK Biobank proxy-cases, and 1.4M controls. We identified 90 independent genome-wide significant signals across 78 loci, including 38 independent risk signals in 37 novel loci. These variants explained 26-36% of the heritable risk of PD. Tests of causality within a Mendelian randomization framework identified putatively causal genes for 70 risk signals. Tissue expression enrichment analysis suggested that signatures of PD loci were heavily brain-enriched, consistent with specific neuronal cell types being implicated from single cell expression data. We found significant genetic correlations with brain volumes, smoking status, and educational attainment. In sum, these data provide the most comprehensive understanding of the genetic architecture of PD to date by revealing many additional PD risk loci, providing a biological context for these risk factors, and demonstrating that a considerable genetic component of this disease remains unidentified.

BackgroundBoth positive and negative associations between higher body mass index (BMI) and Parkinson disease (PD) have been reported in observational studies, but it has been difficult to establish causality because of the possibility of residual confounding or reverse causation. To our knowledge, Mendelian randomisation (MR)—the use of genetic instrumental variables (IVs) to explore causal effects—has not previously been used to test the effect of BMI on PD.Methods and findingsTwo-sample MR was undertaken using genome-wide association (GWA) study data. The associations between the genetic instruments and BMI were obtained from the GIANT consortium and consisted of the per-allele difference in mean BMI for 77 independent variants that reached genome-wide significance. The per-allele difference in log-odds of PD for each of these variants was estimated from a recent meta-analysis, which included 13,708 cases of PD and 95,282 controls. The inverse-variance weighted method was used to estimate a pooled odds ratio (OR) for the effect of a 5-kg/m2 higher BMI on PD. Evidence of directional pleiotropy averaged across all variants was sought using MR–Egger regression. Frailty simulations were used to assess whether causal associations were affected by mortality selection.A combined genetic IV expected to confer a lifetime exposure of 5-kg/m2 higher BMI was associated with a lower risk of PD (OR 0.82, 95% CI 0.69–0.98). MR–Egger regression gave similar results, suggesting that directional pleiotropy was unlikely to be biasing the result (intercept 0.002; p = 0.654). However, the apparent protective influence of higher BMI could be at least partially induced by survival bias in the PD GWA study, as demonstrated by frailty simulations. Other important limitations of this application of MR include the inability to analyse non-linear associations, to undertake subgroup analyses, and to gain mechanistic insights.ConclusionsIn this large study using two-sample MR, we found that variants known to influence BMI had effects on PD in a manner consistent with higher BMI leading to lower risk of PD. The mechanism underlying this apparent protective effect warrants further study.

The human genome contains thousands of natural antisense transcripts (NAT) that can regulate epigenetic state, transcription, RNA stability, or translation of their overlapping genes 1,2 . We describe MAPT-AS1, a primate-conserved, brain-enriched NAT containing an embedded mammalian-wide interspersed repeat (MIR), which represses tau translation by competing with rRNA pairing to MAPT mRNA internal ribosome entry site (IRES) 3 . Tau, a neuronal intrinsically disordered protein (IDP), stabilises axonal microtubules. Hyperphosphorylated, aggregation-prone tau forms the hallmark inclusions of tauopathies 4 . MAPT mutations cause familial frontotemporal dementia (FTLD-tau), and common variation forming the MAPT H1 haplotype is a significant risk factor in many tauopathies 5 , and Parkinson's disease. Notably, expression of MAPT-AS1 or its minimal essential sequences including MIR reduces, whereas silenced MAPT-AS1 increases neuronal tau, and is correlated with tau pathology in human brain. Moreover, we identified hundreds additional NATs with embedded MIRs (MIR-NATs), which are overrepresented at coding genes linked to neurodegeneration, and/or encoding IDPs, and confirmed MIR-NAT-mediated translational control of one such gene, PLCG1. Collectively, we present the importance of MAPT-AS1 for tauopathies, while also uncovering a potentially broad contribution of MIR-NATs to the tightly controlled translation of IDPs 6 , with particular relevance for proteostasis in neurodegeneration.

Key Points Question What genes and genomic processes underlie risk of sporadic Parkinson disease? Findings This genetic association study integrated Parkinson disease genome-wide association study data and brain-derived gene regulation data using various complementary bioinformatic tools and identified 11 candidate genes with evidence of disease-associated regulatory changes. Coexpression and protein level analyses of these genes demonstrated a significant functional association with known mendelian Parkinson disease genes. Meaning This study suggests that gene regulation data may be used to identify candidate genes and pathways involved in sporadic Parkinson disease.

IMPORTANCE Pathogenic variants in LRRK2 are a relatively common genetic cause of Parkinson disease (PD). Currently, the molecular mechanism underlying disease is unknown, and gain and loss of function (LOF) models of pathogenesis have been postulated. LRRK2 variants are reported to result in enhanced phosphorylation of substrates and increased cell death. However, the double knockout of Lrrk2 and its homologue Lrrk1 results in neurodegeneration in a mouse model, suggesting that disease may occur by LOF. Because LRRK2 inhibitors are currently in development as potential disease-modifying treatments in PD, it is critical to determine whether LOF variants in LRRK2 increase or decrease the risk of PD. OBJECTIVE To determine whether LRRK1 and LRRK2 LOF variants contribute to the risk of developing PD. DESIGN, SETTING, AND PARTICIPANTS To determine the prevailing mechanism of LRRK2-mediated disease in human populations, next-generation sequencing data from a large case-control cohort (>23 000 individuals) was analyzed for LOF variants in LRRK1 and LRRK2. Data were generated at 5 different sites and 5 different data sets, including cases with clinically diagnosed PD and neurologically normal control individuals. Data were collected from 2012 through 2017. MAIN OUTCOMES AND MEASURES Frequencies of LRRK1 and LRRK2 LOF variants present in the general population and compared between cases and controls. RESULTS Among 11 095 cases with PD and 12 615 controls, LRRK1 LOF variants were identified in 0.205% of cases and 0.139% of controls (odds ratio, 1.48; SE, 0.571; 95% CI, 0.45-4.44; P = .49) and LRRK2 LOF variants were found in 0.117% of cases and 0.087% of controls (odds ratio, 1.48; SE, 0.431; 95% CI, 0.63-3.50; P = .36). All association tests suggested lack of association between LRRK1 or LRRK2 variants and PD. Further analysis of lymphoblastoid cell lines from several heterozygous LOF variant carriers found that, as expected, LRRK2 protein levels are reduced by approximately half compared with wild-type alleles. CONCLUSIONS AND RELEVANCE Together these findings indicate that haploinsufficiency of LRRK1 or LRRK2 is neither a cause of nor protective against PD. Furthermore, these results suggest that kinase inhibition or allele-specific targeting of mutant LRRK2 remain viable therapeutic strategies in PD.

A BS TRACT: Background: Although the leucine-rich repeat kinase 2 p.G2019S mutation has been demonstrated to be a strong risk factor for PD, factors that contribute to penetrance among carriers, other than aging, have not been well identified. Objectives: To evaluate whether a cumulative genetic risk identified in the recent genome-wide study is associated with penetrance of PD among p.G2019S mutation carriers. Methods: We included p.G2019S heterozygote carriers with European ancestry in three genetic cohorts in which the mutation carriers with and without PD were selectively recruited. We also included the carriers from two data sets: one from a case-control setting without selection of mutation carriers and the other from a population sampling. Associations between polygenic risk score constructed from 89 variants reported recently and PD were tested and meta-analyzed. We also explored the interaction of age and PRS.Results: After excluding eight homozygotes, 833 p. G2019S heterozygote carriers (439 PD and 394 unaffected) were analyzed. Polygenic risk score was associated with a higher penetrance of PD (odds ratio: 1.34; 95% confidence interval: [1.09, 1.64] per +1 standard deviation; P = 0.005). In addition, associations with polygenic risk score and penetrance were stronger in the younger participants (main effect: odds ratio 1.28 [1.04, 1.58] per +1 standard deviation; P = 0.022; interaction effect: odds ratio 0.78 [0.64, 0.94] per +1 standard deviation and + 10 years of age; P = 0.008). Conclusions: Our results suggest that there is a genetic contribution for penetrance of PD among p.G2019S carriers. These results have important etiological consequences and potential impact on the selection of subjects for clinical trials.