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. Author manuscript; available in PMC: 2022 Apr 15.
Published in final edited form as: Neurobiol Aging. 2021 Aug 21;109:264–268. doi: 10.1016/j.neurobiolaging.2021.08.009

RIC3 variants are not associated with Parkinson’s disease in large European, Latin American, or East Asian cohorts

Kajsa Brolin a,*, Sara Bandres-Ciga b, Hampton Leonard b,c,d, Mary B Makarious b,e,f, Cornelis Blauwendraat b, Ignacio F Mata g, Jia Nee Foo h,i, Lasse Pihlstrøm j, Maria Swanberg a, Ziv Gan-Or k,l,m, Manuela MX Tan j, International Parkinson’s Disease Genomics Consortium
PMCID: PMC9011339  NIHMSID: NIHMS1794037  PMID: 34538707

Abstract

Parkinson’s disease (PD) is a complex neurodegenerative disorder in which both rare and common genetic variants contribute to disease risk. Multiple genes have been reported to be linked to monogenic PD but these only explain a fraction of the observed familial aggregation. Rare variants in RIC3 have been suggested to be associated with PD in the Indian population. However, replication studies yielded inconsistent results. We further investigate the role of RIC3 variants in PD in European cohorts using individual-level genotyping data from 14,671 PD patients and 17,667 controls, as well as whole-genome sequencing data from 1,615 patients and 961 controls. We also investigated RIC3 using summary statistics from a Latin American cohort of 1,481 individuals, and from a cohort of 31,575 individuals of East Asian ancestry. We did not identify any association between RIC3 and PD in any of the cohorts. However, more studies of rare variants in non-European ancestry populations, in particular South Asian populations, are necessary to further evaluate the world-wide role of RIC3 in PD etiology.

Keywords: Parkinson’s disease, RIC3, Genetics

1. Introduction

Parkinson’s disease (PD) is a progressive neurodegenerative disorder, for which rhe genetic causes are largely still unknown. Variants in resistance to inhibitors of cholinesterase 3 (RIC3) were first identified in Indian PD patients (Sudhaman et al., 2016). A rare missense variant, p.P57T, was identified in a large Indian PD family with an autosomal-dominant pattern of inheritance, where it was present in all 9 affected individuals and absent in 5 unaffected family members (Sudhaman et al., 2016). Another rare heterozygous missense RIC3 variant, p.V168L, was found in the same study through targeted screening of an independent cohort of 220 Indian PD patients and 186 controls (Sudhaman et al. 2016). However, no association was found between RIC3 variants and PD risk in later studies of French-Canadian and French cohorts (Ross et al., 2017) and a Chinese cohort (He et al., 2017). The RIC3 gene encodes for a member of the resistance to inhibitors of cholinesterase 3-like family. It functions as a chaperone for nicotinic acetylcholine receptors, specifically the alpha-7 subunit of homomeric nicotinic acetylcholine receptors (CHRNA7). These receptors are important for promoting the release of dopamine in the nigrostriatal pathway (Gotti and Clementi, 2004; Quik and Kulak, 2002) where the degeneration and loss of dopaminergic neurons occurs in PD (Breckenridge et al., 2016; Li et al., 2015). The original study showed that both variants in RIC3 reduced the level of these receptors in mutant cell lines (Sudhaman et al., 2016). The authors suggest this is potentially through a dominant-negative effect of the mutations, as the level of CHRNA7 in the mutant cell lines was lower than in untransfected cells (Sudhaman et al., 2016). A further elaboration on the study and on RIC3 can be found in the Supplementary material.

Here, we assessed the role of RIC3 variants in PD risk in several cohorts of different ethnicities: large European cohorts from the International Parkinson’s Disease Genomics Consortium (IPDGC) (Nalls et al., 2019) and the Accelerating Medicines Partnership in Parkinson’s Disease (AMP-PD) (https://amp-pd.org/). We further examined variants in RIC3 in summary statistics from 1,481 individuals from the Latin American Research consortium on the Genetics of PD (LARGE-PD) (Loesch et al., 2021), as well as from 31,575 individuals of East Asian ancestry (Foo et al., 2020).

2. Methods

Summary information of all cohorts can be found in Supplementary Table 1. We analyzed individual-level data from 2 large European datasets of PD patients and controls: IPDGC genome-wide genotyping data (14,671 PD patients and 17,667 controls), and AMP-PD whole-genome sequencing (WGS) data (1615 PD patients and 961 controls after quality control and removal of individuals of non-European ancestry [Supplementary methods]). Analysis and quality control of the AMP-PD dataset (release 1) were performed on the cloud-native platform Terra (https://app.terra.bio/). In each dataset, Genome-Wide Association Study (GWAS) analysis was performed using logistic regression in PLINK v1.9 (Chang et al., 2015). We adjusted for age at study entry, sex, and the first 10 genetic principal components (PCs). Further details can be found in the Supplementary material. In both datasets, we also assessed the burden of rare variants in RIC3, using the tests sequence Kernel association test (SKAT), optimized SKAT (SKAT-O), CMC, Zeggini, Madson-Browning, and Fp in RVTESTS version 2.1.0 (Zhan et al., 2016) under default settings (Supplementary methods). Variants were annotated using the latest available version (October 24, 2019) of ANNOVAR (Wang et al., 2010). Only variants with minor allele frequency (MAF) equal to or less than 3% were defined as rare and included for burden analysis. All code for these analyses is available at: https://github.com/ipdgc/IPDGC-Trainees/blob/master/RIC3.ipynb. Subsequently, associations between variants in RIC3 and PD were investigated in GWAS summary statistics from 2 large additional cohorts, LARGE-PD with 1481 individuals (798 cases and 683 controls) (Loesch et al., 2021), and an Asian PD case-control cohort (Foo et al., 2020). The Asian PD GWAS included 31,575 individuals (6724 PD patients and 24,851 controls) from East Asia, including Singapore/Malaysia, Hong Kong, Taiwan, mainland China, and South Korea (Foo et al., 2020). LocusZoom plots were generated using the LocalZoom tool available at https://my.locuszoom.org.

3. Results

Using IPDGC genotyping data, we identified 143 variants within the RIC3 gene (base pair coordinates 11:8,127,603-8,190,602 in genome build hg19/GRCh37; 11:8,106,056-8,169,055 in build GRCh38). The majority of the variants were non-coding, but 4 common exon variants were identified, including 3 missense variants (Supplementary Table 2). There were no missense variants in the genotyping data with MAF ≤ 3%. Neither of the 2 variants from the original study, p.P57T and p.V168L, were observed in this dataset. None of the RIC3 variants were associated with PD risk after Bonferroni correction (p-value threshold 3.5 × 10−4, Fig. 1A). We performed gene-based burden analysis of RIC3 to assess the cumulative effect of rare variants (MAF ≤ 3%). There was no significant association between RIC3 and PD risk in burden analysis (N variants = 6, CMC p-value = 0.67, Fp p-value = 0.78, Madson-Browning p-value = 0.79, SKAT-O p-value = 0.79, Zeggini p-value = 0.76) (Supplementary Table 3).

Fig. 1.

Fig. 1.

LocusZoom plots of the RIC3 region, showing the p-values and recombination rate of the SNPs analyzed in the (A) IPDGC genotyping data (hg19/GRCh37), (B) AMP-PD WGS data (GRCh38), (C) LARGE-PD GWAS summary statistic data (GRCh38), and (D) East Asian GWAS summary statistic data (hg19/GRCh37). The variant with the lowest p-value from the logistic regression analyses is annotated with a purple diamond in each plot. For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article. Abbreviations: AMP-PD, Accelerating Medicines Partnership in Parkinson’s Disease; Genome-Wide Association Study, GWAS; IPDGC, International Parkinson’s Disease Genomics Consortium; LARGE-PD, Latin American Research consortium on the Genetics of PD; RIC3, resistance to inhibitors of cholinesterase 3; whole genome sequencing, WGS.

In the AMP-PD WGS data, we identified 773 variants in RIC3, including 14 missense variants and 5 synonymous variants (Supplementary Table 4). The 2 variants from the original study were not present in this dataset. No variants were associated with PD risk after Bonferroni correction (p-value threshold 5.1 × 10−5, Fig. 1B).

There was no significant burden of rare variants in RIC3 in association with PD risk using AMP-PD data (N variants = 571, all p-values > 0.05) (Supplementary Table 3). There was also no significant association between RIC3 with PD risk when assessing rare coding variants only (N variants = 15, all p-values > 0.05).

In the LARGE-PD data, 293 variants with a MAF > 1% were identified in RIC3. Also, in LARGE-PD, there were 67 variants with a MAF ≤ 3%. In the Asian dataset, 126 variants were identified with a MAF > 1%. None of the variants in RIC3 in either of the datasets were associated with PD risk (Fig. 1C and D).

4. Discussion

We did not find any association between either single variants or the burden of rare variants in RIC3 and PD risk in the European datasets. These are the largest publicly available European PD case-control cohorts that have been screened for RIC3. We also did not observe any associations between variants in RIC3 and PD in GWAS summary statistics in cohorts from Latin America and East Asia. Overall, we did not find evidence to support the hypothesis that RIC3 is associated with PD risk in individuals of European, Latin American, or East Asian ancestry. This is in line with previous studies, including large PD GWAS meta-analyses (Foo et al., 2020; Loesch et al., 2021; Nalls et al. 2019) and targeted analyses of RIC3 (He et al., 2017; Ross et al., 2017), which also have not found evidence supporting the pathogenicity of RIC3.

However, we cannot rule out the possibility that rare RIC3 variants may be associated with PD in specific families or populations. There was one variant, rs145965152, which had an odds ratio (OR) above 2 and higher frequency in affected verus unaffected individuals in the AMP-PD cohort (Supplementary Table 4). However, this variant was not identified in the other cohorts analyzed, and had a non-significant p-value (p = 0.47) in AMP-PD. We did not identify the 2 originally reported variants p.P57T and p.V168L in WGS data, while the other datasets only included common variants. In addition, it is possible that only a few select rare variants in RIC3 are pathogenic for PD, and this may not be detected by rare variant burden tests if the majority of other rare variants across the gene are not associated with PD.

It is also important to recognize that RIC3 variants were identified in the Indian population as described in the original report, whereas our analysis of rare variants has been conducted in European populations. Only variants with a MAF > 1% were available in the GWAS summary statistics for the datasets of Latin American and Asian ancestry. it is possible that rare variants in this gene have a population-specific effect on PD risk. A GWAS of PD in East Asian populations have identified novel variants for PD risk not found in European GWASs (Foo et al., 2020). in the Genome Aggregation Database (gnomAD) (Karczewski et al., 2020), both of the variants reported in the original study have higher allele frequencies in the South Asian population. For the p.P57T mutation (rs778138358), a total of 14 alleles were identified in 30,596 total alleles (MAF 0.05%). For the p.V168L mutation (rs777471396), 25 alleles were identified in the South Asian population out of a total of 30,614 alleles (MAF 0.08%). One allele carrier for each variant was also identified in the category group ‘Other’ (population not assigned). In contrast, the variants were absent in all other populations on gnomAD, including European, Latino, and East Asian. This suggests that the reported variants in RIC3 are specific to South Asian populations, but does not answer the question of whether they are associated with PD risk, as individuals in gnomAD may not have been systematically screened for PD (Karczewski et al., 2020). Further studies or rare variants in South Asian populations are needed to clarify the role of RIC3 variants in PD etiology.

In summary, we did not find evidence that RIC3 variants are associated with PD risk in European cohorts. We also did not find evidence of more common variants (MAF > 1%) being associated with PD in cohorts from Latin America and East Asia. Based on the varying frequency of RIC3 rare variants across geographic populations, further studies are encouraged of rare variants in RIC3 in non-European populations, in particular South Asian papulations, in order to fully evaluate the suggested role for RIC3 in PD etiology.

Supplementary Material

Supplementary Table 1
Supplementary Table 2
Supplementary Table 3
Supplementary Table 4

Acknowledgements

The authors would like to thank the participants who donated their time and biological samples, making this study possible. We would like to thank all members of the International Parkinson’s Disease Genomic Consortium (IPDGC), the Latin American Research consortium on the Genetics of PD (LARGE-PD), and the East Asian PD GWAS. For a complete overview of members, acknowledgments and funding, please see http://pdgenetics.org/partners and http://large-pd.org/. This work was supported in part by the Norwegian South-East Regional Health Authority (Helse Sør-Øst RHF) (supporting MMXT and LP) and in part by the Swedish Research Council (VR) and Ake Wiberg’s foundation (supporting KB and MS). The authors would also like to thank the Accelerating Medicines Partnership - Parkinson’s disease (AMP-PD). Data used in preparation of this article were obtained from the AMP-PD Knowledge Platform in which the clinical data and biosamples were obtained from the Fox Investigation for New Discovery of Biomarkers (BioFIND), the Harvard Biomarker Study (HBS), the Parkinson’s Progression Markers initiative (PPMI), and the Parkinson’s Disease Biomarkers Program (PDBP). The investigators for each cohort have not participated in reviewing the data analysis or content of the manuscript. More information on the cohorts can be found in the supplementary material. Up-to-date information for AMP-PD can be found at https://www.amp-pd.org.

Footnotes

5.

Disclosure statement

The authors declare that they have no conflict of interest.

6.

CRediT authorship contribution statement

Kajsa Brolin: Formal analysis, Software, Visualization, Writing - original draft. Sara Bandres-Ciga: Conceptualization, Formal analysis, Software, Data Curation, Supervision, Writing - review and editing. Hampton Leonard: Data curation, Software, Writing - review and editing. Mary B. Makarious: Data curation, Software, Writing - review and editing. Cornelis Blauwendraat: Investigation, Software, Data curation, Writing - review and editing. Ignacio F Mata: Investigation, Data curation, Writing - review and editing. Jia Nee Foo: Investigation, Data curation, Writing - review and editing. Lasse Pihlstrøm: Funding acquisition, Writing - review and editing. Maria Swanberg: Funding acquisition, Writing - review and editing. Ziv Gan-Or: Conceptualization, Writing - review and editing. Manuela MX Tan: Formal analysis, Software, Supervision, Writing - original draft.

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.neurobiolaging.2021.08.009.

References

  1. Breckenridge CB, Berry C, Chang ET, Sielken RL, Mandel JS, 2016. Association between Parkinson’s disease and cigarette smoking, rural living, well-water consumption, farming and pesticide use: Systematic review and meta-analysis. PLoS One. 11, 1–42. doi: 10.1371/journal.pone.0151841. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Chang CC, Chow CC, Tellier LCAM, Vattikuti S , Purcell SM, Lee JJ, 2015. Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience. 4, 1–16. doi: 10.1186/s13742-015-0047-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Foo JN, Chew EGY, Chung SJ, Peng R, Blauwendraat C, Nalls MA, Mok KY, Satake W, Toda T, Chao Y, Tan LCS, Tandiono M, Lian MM, Ng EY, Prakash KM, Au WL, Meah WY, Mok SQ, Annuar AA, Chan AYY, Chen L, Chen Y, Jeon BS, Jiang L, Lim JL, Lin JJ, Liu C, Mao C, Mok V, Pei Z, Shang HF, Shi CH, Song K, Tan AH, Wu YR, Xu YM, Xu R, Yan Y, Yang J, Zhang B, Koh WP, Lim SY, Khor CC, Liu J, Tan EK, 2020. Identification of risk loci for Parkinson disease in Asians and comparison of risk between Asians and Europeans: a genome-wide association study. JAMA Neurol 77, 746–754. doi: 10.1001/jamaneurol.2020.0428. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Gotti C, Clementi F, 2004. Neuronal nicotinic receptors: From structure to pathology, Prog. Neurobiol 74, 363–396. doi: 10.1016/j.pneurobio.2004.09.006. [DOI] [PubMed] [Google Scholar]
  5. He D, Hu P, Deng X, Song Z, Yuan L, Yuan X, Deng H 2017. Genetic analysis of the RIC3 gene in Han Chinese patients with Parkinson’s disease. Neurosci. Lett 653, 351–354. doi: 10.1016/j.neulet.2017.06.007. [DOI] [PubMed] [Google Scholar]
  6. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP, Gauthier LD, Brand H, Solomonson M, Watts NA, Rhodes D, Singer-Berk M, England EM, Seaby EG, Kosmicki JA, Walters RK, Tashman K, Farjoun Y, Banks E, Poterba T, Wang A, Seed C, Whiffin N, Chong JX, Samocha KE, Pierce-Hoffman E, Zappala Z, O’Donnell-Luria AH, Minikel EV, Weisburd B, Lek M, Ware JS, Vittal C, Armean IM, Bergelson L, Cibulskis K, Connolly KM, Covarrubias M, Donnelly S, Ferriera S, Gabriel S, Gentry J, Gupta N, Jeandet X, Kaplan D, Llanwarne C, Munshi R, Novod S, Petrillo N, Roazen D, Ruano-Rubio V, Saltzman A, Schleicher M, Soto J, Tibbetts K, Tolonen C, Wade G, Talkowski ME, Aguilar Salinas CA, Ahmad T, Albert CM, Ardissino D, Atzmon G, Barnard J, Beaugerie L, Benjamin EJ, Boehnke M, Bonnycastle LL, Bottinger EP, Bowden DW, Bown MJ, Chambers JC, Chan JC, Chasman D, Cho J, Chung MK, Cohen B, Correa A, Dabelea D, Daly MJ, Darbar D, Duggirala R, Dupuis J, Ellinor PT, Elosua R, Erdmann J, Esko T, Färkkilä M, Florez J, Franke A, Getz G, Glaser B, Glatt SJ, Goldstein D, Gonzalez C, Groop L, Haiman C, Hanis C, Harms M, Hiltunen M, Holi MM, Hultman CM, Kallela M, Kaprio J, Kathiresan S, Kim BJ, Kim YJ, Kirov G, Kooner J, Koskinen S, Krumholz HM, Kugathasan S, Kwak SH, Laakso M, Lehtimäki T, Loos RJF, Lubitz SA, Ma RCW, MacArthur DG, Marrugat J, Mattila KM, McCarroll S, McCarthy MI, Mc-Govern D, McPherson R, Meigs JB, Melander O, Metspalu A, Neale BM, Nilsson PM, O’Donovan MC, Ongur D, Orozco L, Owen MJ, Palmer CNA, Palotie A, Park KS, Pato C, Pulver AE, Rahman N, Remes AM, Rioux JD, Ripatti S, Roden DM, Saleheen D, Salomaa V, Samani NJ, Scharf J, Schunkert H, Shoemaker MB, Sklar P, Soininen H, Sokol H, Spector T, Sullivan PF, Suvisaari J, Tai ES, Teo YY, Tiinamaija T, Tsuang M, Turner D, Tusie-Luna T, Vartiainen E, Ware JS, Watkins H, Weersma RK, Wessman M, Wilson JG, Xavier RJ, Neale BM, Daly MJ, MacArthur DG, 2020. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 581, 434–443. doi: 10.1038/s41586-020-2308-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Li X, Li W, Liu G, Shen X, Tang Y 2015. Association between cigarette smoking and Parkinson’s disease: A meta-analysis. Arch. Gerontol. Geriatr 61, 510–516. doi: 10.1016/j.archger.2015.08.004. [DOI] [PubMed] [Google Scholar]
  8. Loesch DP, Horimoto ARVR, Heilbron K, Sarihan EI, Inca-Martinez M, Mason E, Cornejo-Olivas M, Torres L, Mazzetti P, Cosenrino C, Sarapura-Castro E, Rivera-Valdivia A, Medina AC, Dieguez E, Raggio V, Lescano A, Tumas V, Borges V, Ferraz HB, Rieder CR, Schumacher-Schuh A, Santos-Lobato BL, Velez-Pardo C, Jimenez-Del-Rio M, Lopera F, Moreno S, Chana-Cuevas P, Fernandez W, Arboleda G, Arboleda H, Arboleda-Bustos CE, Yearout D, Zabetian CP, Cannon P, Thornton TA, O’Connor TD, Mata IF on behalf of the Latin American Research Consortium on the Genetics of Parkinson’s Disease (LARGE-PD), 2021. Characterizing the Genetic Architecture of Parkinson’s Disease in Latinos. Ann. Neurol doi: 10.1002/ana.26153. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Nalls MA, Blauwendraat C, Vallerga CL, Heilbron K, Bandres-Ciga S, Chang D, Tan M, Kia DA, Noyce AJ, Xue A, Bras J, Young E, von Coelln R, Simón-Sánchez J, Schulte C, Sharma M, Krohn L, Pihlstrøm L, Siitonen A, Iwaki H, Leonard H, Faghri F, Gibbs JR, Hernandez DG, Scholz SW, Botia JA, Martinez M, Corvol JC, Lesage S, Jankovic J, Shulman LM, Sutherland M, Tienari P, Majamaa K, Toft M, Andreassen OA, Bangale T, Brice A, Yang J, Gan-Or Z, Gasser T, Heutink P, Shulman JM, Wood NW, Hinds DA, Hardy JA, Morris HR, Gratten J, Visscher PM, Graham RR, Singleton AB, Adarmes-Gómez AD, Aguilar M, Aitkulova A, Akhmetzhanov V, Alcalay RN, Alvarez I, Alvarez V, Barrero FJ, Bergareche Yarza JA, Bernal-Bernal I, Billingsley K, Blazquez M, Bonilla-Toribio M, Botía JA, Boungiorno MT, Brockmann K, Bubb V, Buiza-Rueda D, Cámara A, Carrillo F, Carrión-Claro M, Cerdan D, Chelban V, Clarimón J, Clarke C, Compta Y, Cookson MR, Craig DW, Danjou F, Diez-Fairen M, Dols-lcardo O, Duarte J, Duran R, Escamilla-Sevilla F, Escott-Price V, Ezquerra M, Feliz C, Fernández M, Fernández-Santiago R, Finkbeiner S, Foltynie T, Garcia C, García-Ruiz P, Gomez Heredia MJ, Gómez-Garre P, González MM, Gonzalez-Aramburu I, Guelfi S, Guerreiro R, Hardy J, Hassin-Baer S, Hoenicka J, Holmans P, Houlden H, Infante J, Jesús S, Jimenez-Escrig A, Kaishybayeva G, Kaiyrzhanov R, Karimova A, Kinghorn KJ, Koks S, Kulisevsky J, Labrador-Espinosa MA, Leonard HL, Lewis P, Lopez-Sendon JL, Lovering R, Lubbe S, Lungu C, Macias D, Manzoni C, Marín J, Marinus J, Marti MJ, Martínez Torres I, Martínez-Castrillo JC, Mata M, Mencacci NE, Méndezdel-Barrio C, Middlehurst B, Mínguez A, Mir P, Mok KY, Muñoz E, Narendra D, Ojo OO, Okubadejo NU, Pagola AG, Pastor P, Perez Errazquin F, Periñán-Tocino T, Pihlstrom L, Plun-Favreau H, Quinn J, R’Bibo L, Reed X, Rezola EM, Rizig M, Rizzu P, Robak L, Rodriguez AS, Rouleau GA, Ruiz-Martínez J, Ruz C, Ryten M, Sadykova D, Schreglmann S, Shashkin C, Sierra M, Suarez-Sanmartin E, Taba P, Tabernero C, Tan MX, Tartari JP, Tejera-Parrado C, Tolosa E, Trabzuni D, Valldeoriola F, van Hilten JJ, Van Keuren-Jensen K, Vargas-González L, Vela L, Vives F, Williams N, Zharkinbekova N, Zharmukhanov Z, Zholdybayeva E, Zimprich A, Ylikotila P, Shulman LM, Reich S, Savitt J, Agee M, Alipanahi B, Auton A, Bell RK, Bryc K, Elson SL, Fontanillas P, Furlotte NA, Huber KE, Hicks B, Jewett EM, Jiang Y, Kleinman A, Lin KH, Litterman NK, McCreight JC, McIntyre MH, McManus KF, Mountain JL, Noblin ES, Northover CAM, Pitts SJ, Poznik GD, Sathirapongsasuti JF, Shelton JF, Shringarpure S, Tian C, Tung J, Vacic V, Wang X, Wilson CH, Anderson X, Bentley S, Dalrymple-Alford J, Fowdar J, Halliday G, Henders AK, Hickie I, Kassam I, Kennedy M, Kwok J, Lewis S, Mellick G, Montgomery G, Pearson J, Pitcher T, Sidorenko J, Silburn PA, Vallerga CL, Visscher PM, Wray NR, Zhang F, 2019. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol 18, 1091–1102. doi: 10.1016/S1474-4422(19)30320-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Quik M, Kulak JM, 2002. Nicotine and nicotinic receptors; relevance to Parkinson’s disease. Neurotoxicology. 23, 581–594, doi: 10.1016/S0161-813X(02)00036-0. [DOI] [PubMed] [Google Scholar]
  11. Ross JP, Dupré N, Dauvilliers Y, Strong S, Dionne-Laporte A, Dion PA, Rouleau GA, Gan-Or Z, 2017. RIC3 variants are not associated with Parkinson’s disease in French-Canadians and French. Neurobiol. Aging 53, 194.e9–194.e11. doi: 10.1016/j.neurobiolaging.2017.01.005. [DOI] [PubMed] [Google Scholar]
  12. Sudhaman S, Muthane UB, Behari M, Govindappa ST, Juyal RC, Thelma BK, 2016. Evidence of mutations in RIC3 acetylcholine receptor chaperone as a novel cause of autosomal-dominant Parkinson’s disease with non-motor phenotypes. J. Med. Genet 53, 559–566. doi: 10.1136/jmedgenet-2015-103616. [DOI] [PubMed] [Google Scholar]
  13. Wang K, Li M, Hakonarson H 2010. ANNOVAR : functional annotation of genetic variants from high-throughput sequencing data. Nucleic. Acids. Res 38, e164. doi: 10.1093/nar/gkq603. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Zhan X, Hu Y, Li B, Abecasis GR, Liu DJ, 2016. RVTESTS: An efficient and comprehensive tool for rare variant association analysis using sequence data. Bioinformatics. 32, 1423–1426. doi: 10.1093/bioinformatics/btw079. [DOI] [PMC free article] [PubMed] [Google Scholar]

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Supplementary Materials

Supplementary Table 1
Supplementary Table 2
Supplementary Table 3
Supplementary Table 4

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