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Figure.  Genetic Risk Scores (GRSs) for Parkinson Disease (PD) and Bipolar Disorder (BD)
Genetic Risk Scores (GRSs) for Parkinson Disease (PD) and Bipolar Disorder (BD)

A, Parkinson disease GRS violin plots divided by BD cases and controls in the UK Biobank (UKB). B, Bipolar disorder GRS violin plots divided by PD cases and controls from the International Parkinson Disease Genomics Consortium (IPDGC). C, Bipolar disorder GRS violin plots divided by PD cases, bipolar disorder cases with a PD diagnosis (BD-PD), and controls from the UKB.

Table.  Association, Reverse Association, and Sensitivity Analyses for Parkinson Disease and Bipolar Disorder Using 2-Sample Mendelian Randomization
Association, Reverse Association, and Sensitivity Analyses for Parkinson Disease and Bipolar Disorder Using 2-Sample Mendelian Randomization
1.
Faustino  PR, Duarte  GS, Chendo  I,  et al.  Risk of developing Parkinson disease in bipolar disorder: a systematic review and meta-analysis.   JAMA Neurol. Published online October 14, 2019. doi:10.1001/jamaneurol.2019.3446PubMedGoogle Scholar
2.
Bulik-Sullivan  BK, Loh  P-R, Finucane  HK,  et al; Schizophrenia Working Group of the Psychiatric Genomics Consortium.  LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.   Nat Genet. 2015;47(3):291-295. doi:10.1038/ng.3211PubMedGoogle ScholarCrossref
3.
Nalls  MA, Blauwendraat  C, Vallerga  CL,  et al.  Expanding Parkinson’s disease genetics: novel risk loci, genomic context, causal insights and heritable risk.   bioRxi. Preprint. Posted online March 4, 2019. doi:10.1101/388165Google Scholar
4.
Stahl  EA, Breen  G, Forstner  AJ,  et al; eQTLGen Consortium; BIOS Consortium; Bipolar Disorder Working Group of the Psychiatric Genomics Consortium.  Genome-wide association study identifies 30 loci associated with bipolar disorder.   Nat Genet. 2019;51(5):793-803. doi:10.1038/s41588-019-0397-8PubMedGoogle ScholarCrossref
5.
Nalls  MA, Pankratz  N, Lill  CM,  et al; International Parkinson’s Disease Genomics Consortium (IPDGC); Parkinson’s Study Group (PSG) Parkinson’s Research: The Organized GENetics Initiative (PROGENI); 23andMe; GenePD; NeuroGenetics Research Consortium (NGRC); Hussman Institute of Human Genomics (HIHG); Ashkenazi Jewish Dataset Investigator; Cohorts for Health and Aging Research in Genetic Epidemiology (CHARGE); North American Brain Expression Consortium (NABEC); United Kingdom Brain Expression Consortium (UKBEC); Greek Parkinson’s Disease Consortium; Alzheimer Genetic Analysis Group.  Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson’s disease.   Nat Genet. 2014;46(9):989-993. doi:10.1038/ng.3043PubMedGoogle ScholarCrossref
6.
de Lau  LML, Breteler  MMB.  Epidemiology of Parkinson’s disease.   Lancet Neurol. 2006;5(6):525-535. doi:10.1016/S1474-4422(06)70471-9PubMedGoogle ScholarCrossref
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    Research Letter
    May 11, 2020

    Assessment of Genetic Association Between Parkinson Disease and Bipolar Disorder

    Author Affiliations
    • 1Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland
    JAMA Neurol. 2020;77(8):1034-1035. doi:10.1001/jamaneurol.2020.0248

    A recent systematic review and meta-analysis1 suggests that a previous diagnosis of bipolar disorder (BD) increases the likelihood of a subsequent diagnosis of idiopathic Parkinson disease (PD). Using data from the International Parkinson Disease Genomics Consortium (IPDGC) and the UK Biobank (UKBB), we assessed the possibility of a shared genetic etiology or association between PD and BD.

    Methods

    To explore overlapping genetics, linkage disequilibrium score regression (LDSC)2 was performed using recent genome-wide association study (GWAS) meta-analyses in PD3 and BD.4 We applied the genetic risk score (GRS) as previously described5 using 86 of the 90 independent risk loci associated with PD (imputation quality R2, >0.3)3 to assess BD status in 1106 patients with BD and 9980 controls (who were of European ancestry without BD, PD, or a first-degree relative affected by PD) with available imputed data from the UKBB. We applied the GRS using 27 of 30 risk loci associated with BD4 to determine the association of PD status in 19 249 patients with PD and 22 875 controls from the clinically ascertained subset of IPDGC. We applied BD GRS to explore the association with PD in the UKBB data. All data included from published IPDGC GWAS studies complied with the relevant institutional review boards. Software used was plink 1.9 and threshold for significance = .05. We also explored the direction of the association between PD and BD in a mendelian randomization (MR) analysis in an attempt to draw potential causal inferences.

    Results

    In the LDSC analysis, no evidence for a genetic correlation between both diseases was found (rg = 0.068; SE = 0.04;P = .10). This null association persisted when restricting the analysis to only clinically confirmed PD cases. Although bias might be a concern, LDSC intercepts were close to 1 in PD and BD summary statistics (PD: h2 = 0.221; SE = 0.021; intercept = 0.97; BD: h2 = 0.346; SE = 0.016; intercept = 1.01).

    The GRS for PD was not associated with BD status (β = 0.036; SE = 0.04; P = .36). No differences were found when stratifying BD cases with or without a diagnosis of PD (Figure A). The GRS for PD was significantly associated with PD status in the UKBB data (β = 0.041; SE = 0.016; P = .01). The GRS for BD was not associated with PD status in the IPDGC cohort (β = 0.015; SE = 0.011; P =.15) (Figure B).

    When we stratified PD cases with and without a BD diagnosis, BD GRS was not associated with PD status in the PD group without a diagnosis of BD (β = 0.012; SE = 0.026; P = .63; sampling 1696 cases and 9980 controls) (Figure B). In 30 samples with PD and BD, there was a higher mean GRS than controls (t test, P = .002) that was perhaps driven by high variance in the very small sample size of this subset. The GRS for BD was significantly associated with BD status in the UKBB data (β = 0.12; SE = 0.03; P = .001). In MR analysis, we were not able to infer a potential a causal association between PD and BD in either direction (Table) despite having sufficient statistical power to prove the null hypothesis (F statistic = 89.73) (Table).

    Discussion

    Parkinson disease affects approximately 1% of the population older than 60 years.6 The mean (SD) age at recruitment for the BD UKBB cohort (code F31) was 61.65 (5.94) years and the prevalence for PD was 2.7% (30/1106). This indicates a higher prevalence of PD in concordance with the published meta-analysis.1 Based on what to our knowledge are the largest PD and BD genetic data sets to date, this analysis of common variability suggests no genetic association between both diseases. However, we believe that the original study was well powered and supported by higher estimates of PD among BD in the UKBB cohort. Limitations of this study include that important pleiotropic associations may have been overlooked when using LDSC, and that additional external factors, rare or structural genetic variation, and indirect unmeasured confounding may have contributed to this co-occurrence. Notably, a portion of the diagnosed PD cases in the original BD cohort may represent misattribution of drug-induced parkinsonism, driven by the use of commonly applied medications in BD (a potential limitation that Faustino and colleagues1 acknowledge).

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    Article Information

    Accepted for Publication: January 17, 2020.

    Corresponding Author: Sara Bandres-Ciga, PhD, Porter Neuroscience Center, 35 Convent Dr, Bethesda, MD 20892 (sara.bandresciga@nih.gov).

    Published Online: May 11, 2020. doi:10.1001/jamaneurol.2020.0248

    Author Contributions: Dr Bandres-Ciga had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Bandres-Ciga, Singleton.

    Acquisition, analysis, or interpretation of data: Bandres-Ciga, Blauwendraat.

    Drafting of the manuscript: Bandres-Ciga.

    Critical revision of the manuscript for important intellectual content: Blauwendraat, Singleton.

    Statistical analysis: Bandres-Ciga, Blauwendraat.

    Supervision: Singleton.

    Conflict of Interest Disclosures: None reported.

    Funding/Support: This work was supported in part by the Intramural Research Programs of the National Institute on Aging (Z01-AG000949-02).

    Role of the Funder/Sponsor: The National Institute on Aging had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Additional Contributions: We thank all of the participants who donated their time and biological samples to be part of this study. We also thank all members of the International Parkinson Disease Genomics Consortium. None of these individuals were compensated for their contributions.

    Additional Information: For a complete overview of members, please see https://pdgenetics.org/partners. This research has been conducted using the UK Biobank Resource under application number 33601. This study used the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health (http://hpc.nih.gov).

    References
    1.
    Faustino  PR, Duarte  GS, Chendo  I,  et al.  Risk of developing Parkinson disease in bipolar disorder: a systematic review and meta-analysis.   JAMA Neurol. Published online October 14, 2019. doi:10.1001/jamaneurol.2019.3446PubMedGoogle Scholar
    2.
    Bulik-Sullivan  BK, Loh  P-R, Finucane  HK,  et al; Schizophrenia Working Group of the Psychiatric Genomics Consortium.  LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.   Nat Genet. 2015;47(3):291-295. doi:10.1038/ng.3211PubMedGoogle ScholarCrossref
    3.
    Nalls  MA, Blauwendraat  C, Vallerga  CL,  et al.  Expanding Parkinson’s disease genetics: novel risk loci, genomic context, causal insights and heritable risk.   bioRxi. Preprint. Posted online March 4, 2019. doi:10.1101/388165Google Scholar
    4.
    Stahl  EA, Breen  G, Forstner  AJ,  et al; eQTLGen Consortium; BIOS Consortium; Bipolar Disorder Working Group of the Psychiatric Genomics Consortium.  Genome-wide association study identifies 30 loci associated with bipolar disorder.   Nat Genet. 2019;51(5):793-803. doi:10.1038/s41588-019-0397-8PubMedGoogle ScholarCrossref
    5.
    Nalls  MA, Pankratz  N, Lill  CM,  et al; International Parkinson’s Disease Genomics Consortium (IPDGC); Parkinson’s Study Group (PSG) Parkinson’s Research: The Organized GENetics Initiative (PROGENI); 23andMe; GenePD; NeuroGenetics Research Consortium (NGRC); Hussman Institute of Human Genomics (HIHG); Ashkenazi Jewish Dataset Investigator; Cohorts for Health and Aging Research in Genetic Epidemiology (CHARGE); North American Brain Expression Consortium (NABEC); United Kingdom Brain Expression Consortium (UKBEC); Greek Parkinson’s Disease Consortium; Alzheimer Genetic Analysis Group.  Large-scale meta-analysis of genome-wide association data identifies six new risk loci for Parkinson’s disease.   Nat Genet. 2014;46(9):989-993. doi:10.1038/ng.3043PubMedGoogle ScholarCrossref
    6.
    de Lau  LML, Breteler  MMB.  Epidemiology of Parkinson’s disease.   Lancet Neurol. 2006;5(6):525-535. doi:10.1016/S1474-4422(06)70471-9PubMedGoogle ScholarCrossref
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