The Parkinson’s Disease Mendelian Randomization Research Portal

ABSTRACTBackgroundMendelian randomization (MR) is a method for exploring observational associations to find evidence of causality.ObjectiveTo apply MR between multiple risk factors/phenotypic traits (exposures) and Parkinson’s disease (PD) in a large, unbiased manner, and to create a public resource for research.MethodsWe used two-sample MR in which the summary statistics relating to SNPs from genome wide association studies (GWASes) of 5,839 exposures curated on MR Base were used to assess causal relationships with PD. We selected the highest quality exposure GWASes for this report (n=401). For the disease outcome, summary statistics from the largest published PD GWAS were used. For each exposure, the causal effect on PD was assessed using the inverse variance weighted (IVW) method, followed by a range of sensitivity analyses. We used a false discovery rate (FDR) corrected p-value of <0.05 from the IVW analysis to prioritize traits of interest.ResultsWe observed evidence for causal associations between twelve exposures and risk of PD. Of these, nine were causal effects related to increasing adiposity and decreasing risk of PD. The remaining top exposures that affected PD risk were tea drinking, time spent watching television and forced vital capacity, but the latter two appeared to be biased by violations of underlying MR assumptions.DiscussionWe present a new platform which offers MR analyses for a total of 5,839 GWASes versus the largest PD GWASes available (https://pdgenetics.shinyapps.io/pdgenetics/). Alongside, we report further evidence to support a causal role for adiposity on lowering the risk of PD.

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Rheumatoid arthritis decreases risk for Parkinson’s disease: a Mendelian randomization study

npj Parkinson s Disease ◽

10.1038/s41531-021-00166-x ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

ChunYu Li ◽

RuWei Ou ◽

HuiFang Shang

Keyword(s):

Rheumatoid Arthritis ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Genetic Correlation ◽

Mendelian Randomization ◽

Association Studies ◽

Sensitivity Analyses ◽

Genome Wide Association Studies ◽

Standard Deviation Increase ◽

Rheumatoid Arthritis Risk

AbstractEpidemiological and clinical studies have suggested comorbidity between rheumatoid arthritis and Parkinson’s disease (PD), but whether there exists a causal association and the effect direction of rheumatoid arthritis on PD is controversial and elusive. To evaluate the causal relationship, we first estimated the genetic correlation between rheumatoid arthritis and PD, and then performed a two-sample Mendelian randomization analysis based on summary statistics from large genome-wide association studies of rheumatoid arthritis (N = 47,580) and PD (N = 482,703). We identified negative and significant correlation between rheumatoid arthritis and PD (genetic correlation: −0.10, P = 0.0033). Meanwhile, one standard deviation increase in rheumatoid arthritis risk was associated with a lower risk of PD (OR: 0.904, 95% CI: 0.866–0.943, P: 2.95E–06). The result was robust under all sensitivity analyses. Our results provide evidence supporting a protective role of rheumatoid arthritis on PD. A deeper understanding of the inflammation and immune response is likely to elucidate the potential pathogenesis of PD and identify therapeutic targets for PD.

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Causal Association between Periodontitis and Parkinson’s Disease: A Bidirectional Mendelian Randomization Study

Genes ◽

10.3390/genes12050772 ◽

2021 ◽

Vol 12 (5) ◽

pp. 772

Author(s):

João Botelho ◽

Vanessa Machado ◽

José João Mendes ◽

Paulo Mascarenhas

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Instrumental Variables ◽

Mendelian Randomization ◽

Association Studies ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Genetic Liability ◽

Bidirectional Association

The latest evidence revealed a possible association between periodontitis and Parkinson’s disease (PD). We explored the causal relationship of this bidirectional association through two-sample Mendelian randomization (MR) in European ancestry populations. To this end, we used openly accessible data of genome-wide association studies (GWAS) on periodontitis and PD. As instrumental variables for periodontitis, seventeen single-nucleotide polymorphisms (SNPs) from a GWAS of periodontitis (1817 periodontitis cases vs. 2215 controls) and eight non-overlapping SNPs of periodontitis from an additional GWAS for validation purposes. Instrumental variables to explore for the reverse causation included forty-five SNPs from a GWAS of PD (20,184 cases and 397,324 controls). Multiple approaches of MR were carried-out. There was no evidence of genetic liability of periodontitis being associated with a higher risk of PD (B = −0.0003, Standard Error [SE] 0.0003, p = 0.26). The eight independent SNPs (B = −0.0000, SE 0.0001, p = 0.99) validated this outcome. We also found no association of genetically primed PD towards periodontitis (B = −0.0001, SE 0.0001, p = 0.19). These MR study findings do not support a bidirectional causal genetic liability between periodontitis and PD. Further GWAS studies are needed to confirm the consistency of these results.

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Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations

International Journal of Epidemiology ◽

10.1093/ije/dyaa266 ◽

2021 ◽

Author(s):

Fernando Pires Hartwig ◽

Kate Tilling ◽

George Davey Smith ◽

Deborah A Lawlor ◽

Maria Carolina Borges

Keyword(s):

Waist Circumference ◽

Genetic Variants ◽

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

Real Data ◽

Sensitivity Analyses ◽

Effect Estimate ◽

Genome Wide Association Studies ◽

Residual Confounding

Abstract Background Two-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables. Methods We performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR. Results In the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index. Conclusions Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution.

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Univariable and Multivariable Two-Sample Mendelian Randomization Investigating the Effects of Leisure Sedentary Behaviors on the Risk of Lung Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.742718 ◽

2021 ◽

Vol 12 ◽

Author(s):

Haoxin Peng ◽

Xiangrong Wu ◽

Yaokai Wen ◽

Yiyuan Ao ◽

Yutian Li ◽

...

Keyword(s):

Lung Cancer ◽

Computer Use ◽

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

Sensitivity Analyses ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Sedentary Behaviors ◽

Watching Tv

Background:Leisure sedentary behaviors (LSB) are widespread, and observational studies have provided emerging evidence that LSB play a role in the development of lung cancer (LC). However, the causal inference between LSB and LC remains unknown.Methods: We utilized univariable (UVMR) and multivariable two-sample Mendelian randomization (MVMR) analysis to disentangle the effects of LSB on the risk of LC. MR analysis was conducted with genetic variants from genome-wide association studies of LSB (408,815 persons from UK Biobank), containing 152 single-nucleotide polymorphisms (SNPs) for television (TV) watching, 37 SNPs for computer use, and four SNPs for driving, and LC from the International Lung Cancer Consortium (11,348 cases and 15,861 controls). Multiple sensitivity analyses were further performed to verify the causality.Results: UVMR demonstrated that genetically predisposed 1.5-h increase in LSB spent on watching TV increased the odds of LC by 90% [odds ratio (OR), 1.90; 95% confidence interval (CI), 1.44–2.50; p < 0.001]. Similar trends were observed for squamous cell lung cancer (OR, 1.97; 95%CI, 1.31–2.94; p = 0.0010) and lung adenocarcinoma (OR, 1.64; 95%CI 1.12–2.39; p = 0.0110). The causal effects remained significant after adjusting for education (OR, 1.97; 95%CI, 1.44–2.68; p < 0.001) and body mass index (OR, 1.86; 95%CI, 1.36–2.54; p < 0.001) through MVMR approach. No association was found between prolonged LSB spent on computer use and driving and LC risk. Genetically predisposed prolonged LSB was additionally correlated with smoking (OR, 1.557; 95%CI, 1.287–1.884; p < 0.001) and alcohol consumption (OR, 1.010; 95%CI, 1.004–1.016; p = 0.0016). Consistency of results across complementary sensitivity MR methods further strengthened the causality.Conclusion: Robust evidence was demonstrated for an independent, causal effect of LSB spent on watching TV in increasing the risk of LC. Further work is necessary to investigate the potential mechanisms.

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Risky behaviors and Parkinson’s disease: A Mendelian randomization study in up to 1 million study participants

10.1101/446807 ◽

2018 ◽

Cited By ~ 4

Author(s):

Sandeep Grover ◽

Greco M Fabiola Del ◽

Meike Kasten ◽

Christine Klein ◽

Christina M. Lill ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Standard Deviation ◽

Mendelian Randomization ◽

Risky Behaviors ◽

Sexual Partners ◽

P Value ◽

Genome Wide Association Studies ◽

Standard Deviation Increase ◽

Number Of Sexual Partners

AbstractObjectiveDopaminergic neurotransmission is known to be a potential modulator of risky behaviors including substance abuse, promiscuity, and gambling. Furthermore, observational studies have shown associations between risky behaviors and Parkinson’s disease; however, the causal nature of these associations remains unclear. Thus, in this study, we examine causal associations between risky behavior phenotypes on Parkinson’s disease using a Mendelian randomization approach.MethodsWe used two-sample Mendelian randomization to generate unconfounded estimates using summary statistics from two independent, large meta-analyses of genome-wide association studies on risk taking behaviors (n=370,771-939,908) and Parkinson’s disease (cases: n=9581, controls: n = 33,245). We used inverse variance weighted as the main method for judging causality.ResultsOur results support a strong protective association between the tendency to smoke and Parkinson’s disease (OR=0.714 per log odds of ever smoking; 95% CI=0.568-0.897; p-value=0.0041; Cochran Q test; p-value=0.238; I2 index=6.3%). Furthermore, we observed risk association trends between automobile speed propensity as well as the number of sexual partners and Parkinson’s disease after removal of overlapping loci with other risky traits (OR=1.986 for each standard deviation increase in normalized automobile speed propensity; 95% CI=1.215-3.243; p-value=0.0066, OR=1.635 for each standard deviation increase in number of sexual partners; 95% CI=1.165-2.293; p-value=0.0049).InterpretationThese findings provide support for a causal relationship between general risk tolerance and Parkinson’s disease and may provide new insights in the pathogenic mechanisms leading to the development of Parkinson’s disease.

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An iterative approach to detect pleiotropy and perform Mendelian Randomization analysis using GWAS summary statistics

Bioinformatics ◽

10.1093/bioinformatics/btaa985 ◽

2020 ◽

Author(s):

Xiaofeng Zhu ◽

Xiaoyin Li ◽

Rong Xu ◽

Tao Wang

Keyword(s):

Complex Traits ◽

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

Real Data ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Causal Relationships ◽

Multiple Traits

Abstract Motivation The overall association evidence of a genetic variant with multiple traits can be evaluated by cross-phenotype association analysis using summary statistics from genome-wide association studies. Further dissecting the association pathways from a variant to multiple traits is important to understand the biological causal relationships among complex traits. Results Here, we introduce a flexible and computationally efficient Iterative Mendelian Randomization and Pleiotropy (IMRP) approach to simultaneously search for horizontal pleiotropic variants and estimate causal effect. Extensive simulations and real data applications suggest that IMRP has similar or better performance than existing Mendelian Randomization methods for both causal effect estimation and pleiotropic variant detection. The developed pleiotropy test is further extended to detect colocalization for multiple variants at a locus. IMRP will greatly facilitate our understanding of causal relationships underlying complex traits, in particular, when a large number of genetic instrumental variables are used for evaluating multiple traits. Availability and implementation The software IMRP is available at https://github.com/XiaofengZhuCase/IMRP. The simulation codes can be downloaded at http://hal.case.edu/∼xxz10/zhu-web/ under the link: MR Simulations software. Supplementary information Supplementary data are available at Bioinformatics online.

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Shared Genetic Background between Parkinson’s Disease and Schizophrenia: A Two-Sample Mendelian Randomization Study

Brain Sciences ◽

10.3390/brainsci11081042 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1042

Author(s):

Kiwon Kim ◽

Soyeon Kim ◽

Woojae Myung ◽

Injeong Shim ◽

Hyewon Lee ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Genetic Variants ◽

Mendelian Randomization ◽

Autism Spectrum ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Increased Risk ◽

Extrapyramidal Signs ◽

Shared Genetic

Background and objectives: Parkinson’s disease (PD) and schizophrenia often share symptomatology. Psychotic symptoms are prevalent in patients with PD, and similar motor symptoms with extrapyramidal signs are frequently observed in antipsychotic-naïve patients with schizophrenia as well as premorbid families. However, few studies have examined the relationship between PD and schizophrenia. We performed this study to evaluate whether genetic variants which increase PD risk influence the risk of developing schizophrenia, and vice versa. Materials and Methods: Two-sample Mendelian randomization (TSMR) with summary statistics from large-scale genome-wide association studies (GWAS) was applied. Summary statistics were extracted for these instruments from GWAS of PD and schizophrenia; Results: We found an increase in the risk of schizophrenia per one-standard deviation (SD) increase in the genetically-predicted PD risk (inverse-variance weighted method, odds ratio = 1.10; 95% confidence interval, 1.05−1.15; p = 3.49 × 10−5). The association was consistent in sensitivity analyses, including multiple TSMR methods, analysis after removing outlier variants with potential pleiotropic effects, and analysis after applying multiple GWAS subthresholds. No relationships were evident between PD and smoking or other psychiatric disorders, including attention deficit hyperactivity disorder, autism spectrum disorder, bipolar affective disorder, major depressive disorder, Alzheimer’s disease, or alcohol dependence. However, we did not find a reverse relationship; genetic variants increasing schizophrenia risk did not alter the risk of PD; Conclusions: Overall, our findings suggest that increased genetic risk of PD can be associated with increased risk of schizophrenia. This association supports the intrinsic nature of the psychotic symptom in PD rather than medication or environmental effects. Future studies for possible comorbidities and shared genetic structure between the two diseases are warranted.

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Correction for sample overlap, winner's curse and weak instrument bias in two-sample Mendelian Randomization

10.1101/2021.03.26.437168 ◽

2021 ◽

Author(s):

Ninon Mounier ◽

Zoltan Kutalik

Keyword(s):

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

Simulated Data ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Winner's Curse ◽

Weak Instruments ◽

Winner’S Curse ◽

Wide Range

Inverse-variance weighted two-sample Mendelian Randomization (IVW-MR) is the most widely used approach that uses genome-wide association studies summary statistics to infer the existence and strength of the causal effect between an exposure and an outcome. Estimates from this approach can be subject to different biases due to: (i) the overlap between the exposure and outcome samples; (ii) the use of weak instruments and winner's curse. We developed a method that aims at tackling all these biases together. Assuming spike-and-slab genomic architecture and leveraging LD-score regression and other techniques, we could analytically derive and reliably estimate the bias of IVW-MR using association summary statistics only. This allowed us to apply a bias correction to IVW-MR estimates, which we tested using simulated data for a wide range of realistic scenarios. In all the explored scenarios, our correction reduced the bias, in some situations by as much as 30 folds. When applied to real data on obesity-related exposures, we observed significant differences between IVW-based and corrected effects, both for non-overlapping and fully overlapping samples. While most studies are extremely careful to avoid any sample overlap when performing two-sample MR analysis, we have demonstrated that the incurred bias is much less substantial than the one due to weak instruments or winner's curse, which are often ignored.

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The putative causal effect of type 2 diabetes in risk of cataract: a Mendelian randomization study in East Asian

10.1101/2021.02.08.430342 ◽

2021 ◽

Author(s):

Haoyang Zhang ◽

Xuehao Xiu ◽

Angli Xue ◽

Yuedong Yang ◽

Yuanhao Yang ◽

...

Keyword(s):

Type 2 Diabetes ◽

Genetic Correlation ◽

Mendelian Randomization ◽

Causal Effect ◽

Association Studies ◽

Population Based ◽

P Value ◽

Genome Wide Association Studies ◽

Genome Wide

AbstractBackgroundThe epidemiological association between type 2 diabetes and cataract has been well-established. However, it remains unclear whether the two diseases share a genetic basis, and if so, whether this reflects a causal relationship.MethodsWe utilized East Asian population-based genome-wide association studies (GWAS) summary statistics of type 2 diabetes (Ncase=36,614, Ncontrol=155,150) and cataract (Ncase=24,622, Ncontrol=187,831) to comprehensively investigate the shared genetics between the two diseases. We performed 1. linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (ρ-HESS) to estimate the genetic correlation and local genetic correlation between type 2 diabetes and cataract; 2. multiple Mendelian randomization (MR) analyses to infer the putative causality between type 2 diabetes and cataract; and 3. Summary-data-based Mendelian randomization (SMR) to identify candidate risk genes underling the causality.ResultsWe observed a strong genetic correlation (rg=0.58; p-value=5.60×10−6) between type 2 diabetes and cataract. Both ρ-HESS and multiple MR methods consistently showed a putative causal effect of type 2 diabetes on cataract, with estimated liability-scale MR odds ratios (ORs) at around 1.10 (95% confidence interval [CI] ranging from 1.06 to 1.17). In contrast, no evidence supports a causal effect of cataract on type 2 diabetes. SMR analysis identified two novel genes MIR4453HG (βSMR=−0.34, p-value=6.41×10−8) and KCNK17 (βSMR=−0.07, p-value=2.49×10−10), whose expression levels were likely involved in the putative causality of type 2 diabetes on cataract.ConclusionsOur results provided robust evidence supporting a causal effect of type 2 diabetes on the risk of cataract in East Asians, and posed new paths on guiding prevention and early-stage diagnosis of cataract in type 2 diabetes patients.Key MessagesWe utilized genome-wide association studies of type 2 diabetes and cataract in a large Japanese population-based cohort and find a strong genetic overlap underlying the two diseases.We performed multiple Mendelian randomization models and consistently disclosed a putative causal effect of type 2 diabetes on the development of cataract.We revealed two candidate genes MIR4453HG and KCNK17 whose expression levelss are likely relevant to the causality between type 2 diabetes and cataract.Our study provided theoretical fundament at the genetic level for improving early diagnosis, prevention and treatment of cataract in type 2 diabetes patients in clinical practice

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Unhealthy Behaviours and Risk of Parkinson’s Disease: A Mendelian Randomisation Study

Journal of Parkinson s Disease ◽

10.3233/jpd-202487 ◽

2021 ◽

pp. 1-13

Author(s):

Karl Heilbron ◽

Melanie P. Jensen ◽

Sara Bandres-Ciga ◽

Pierre Fontanillas ◽

Cornelis Blauwendraat ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Protective Effect ◽

Tobacco Smoking ◽

Alcohol Intake ◽

Association Studies ◽

Sensitivity Analyses ◽

Genome Wide Association Studies ◽

Split Sample ◽

The Impact

Background: Tobacco smoking and alcohol intake have been identified in observational studies as potentially protective factors against developing Parkinson’s disease (PD); the impact of body mass index (BMI) on PD risk is debated. Whether such epidemiological associations are causal remains unclear. Mendelian randomsation (MR) uses genetic variants to explore the effects of exposures on outcomes; potentially reducing bias from residual confounding and reverse causation. Objective: Using MR, we examined relationships between PD risk and three unhealthy behaviours: tobacco smoking, alcohol intake, and higher BMI. Methods: 19,924 PD cases and 2,413,087 controls were included in the analysis. We performed genome-wide association studies to identify single nucleotide polymorphisms associated with tobacco smoking, alcohol intake, and BMI. MR analysis of the relationship between each exposure and PD was undertaken using a split-sample design. Results: Ever-smoking reduced the risk of PD (OR 0.955; 95%confidence interval [CI] 0.921–0.991; p = 0.013). Higher daily alcohol intake increased the risk of PD (OR 1.125, 95%CI 1.025–1.235; p = 0.013) and a 1 kg/m2 higher BMI reduced the risk of PD (OR 0.988, 95%CI 0.979–0.997; p = 0.008). Sensitivity analyses did not suggest bias from horizontal pleiotropy or invalid instruments. Conclusion: Using split-sample MR in over 2.4 million participants, we observed a protective effect of smoking on risk of PD. In contrast to observational data, alcohol consumption appeared to increase the risk of PD. Higher BMI had a protective effect on PD, but the effect was small.

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