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Circulating Proteins Influencing Psychiatric Disease: A Mendelian Randomization Study

  • Tianyuan Lu
    Affiliations
    Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada

    Quantitative Life Sciences Program, McGill University, Montreal, Canada
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  • Vincenzo Forgetta
    Affiliations
    Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada
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  • Celia M.T. Greenwood
    Affiliations
    Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada

    Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada

    Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada

    Department of Human Genetics, McGill University, Montreal, Canada
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  • Sirui Zhou
    Affiliations
    Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada

    Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada

    Department of Human Genetics, McGill University, Montreal, Canada
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  • J Brent Richards
    Correspondence
    Correspondence to: J Brent Richards ; Address; Jewish General Hospital, Room H-413, 3755 Chemin de la Côte-Sainte-Catherine, Montréal, Québec, H3T 1E2, Canada; Tel: 514-340-8222 ext. 4362
    Affiliations
    Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada

    Department of Human Genetics, McGill University, Montreal, Canada

    Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
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      Abstract

      Background

      There is a pressing need of novel drug targets for psychiatric disorders. Circulating proteins are potential candidates because they are relatively easy to measure and modulate, and play important roles in signaling.

      Methods

      We performed two-sample Mendelian randomization (MR) analyses to estimate the associations between circulating protein abundances and risk of ten psychiatric disorders. Genetic variants associated with 1,611 circulating protein abundances identified in six large-scale proteomic studies were used as genetic instruments. Effects of circulating proteins on psychiatric disorders were estimated by Wald ratio or inverse variance-weighted ratio tests. Horizontal pleiotropy, colocalization, and protein-altering effects were examined to validate assumptions of MR.

      Results

      Nine circulating protein-to-disease associations withstood multiple sensitivity analyses. Among them, two circulating proteins had associations replicated in three proteomic studies. A one standard deviation increase in genetically predicted circulating TIMP4 level was associated with a reduced risk of anorexia nervosa (minimum odds ratio (OR) = 0.83; 95% confidence interval (CI): 0.76-0.91) and bipolar disorder (minimum OR = 0.88; 95% CI: 0.82-0.94). A one standard deviation increase in genetically predicted circulating ESAM level was associated with an increased risk of schizophrenia (maximum OR = 1.32; 95% CI: 1.22-1.43). In addition, 58 suggestive protein-to-disease associations warrant validations with observational or experimental evidence. For instance, a standard deviation increase in ERLEC1-201-to-ERLEC1-202 splice variant ratio was associated with a reduced risk of schizophrenia (OR = 0.94; 95% CI: 0.90-0.97).

      Conclusions

      Prioritized circulating proteins appear to influence risk of psychiatric disease and may be explored as intervention targets.

      Keywords

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      References

        • Sullivan P.F.
        • Daly M.J.
        • O'Donovan M.
        Genetic architectures of psychiatric disorders: the emerging picture and its implications.
        Nat Rev Genet. 2012; 13: 537-551
        • Collins P.Y.
        • Patel V.
        • Joestl S.S.
        • March D.
        • Insel T.R.
        • Daar A.S.
        • et al.
        Grand challenges in global mental health.
        Nature. 2011; 475: 27-30
        • Rehm J.
        • Shield K.D.
        Global Burden of Disease and the Impact of Mental and Addictive Disorders.
        Curr Psychiatry Rep. 2019; 21: 10
        • Krystal J.H.
        • State M.W.
        Psychiatric disorders: diagnosis to therapy.
        Cell. 2014; 157: 201-214
        • Geschwind D.H.
        • Flint J.
        Genetics and genomics of psychiatric disease.
        Science. 2015; 349: 1489-1494
        • Schmidt A.F.
        • Finan C.
        • Gordillo-Maranon M.
        • Asselbergs F.W.
        • Freitag D.F.
        • Patel R.S.
        • et al.
        Genetic drug target validation using Mendelian randomisation.
        Nat Commun. 2020; 11: 3255
        • Dohrenwend B.P.
        • Levav I.
        • Shrout P.E.
        • Schwartz S.
        • Naveh G.
        • Link B.G.
        • et al.
        Socioeconomic status and psychiatric disorders: the causation-selection issue.
        Science. 1992; 255: 946-952
        • Skrivankova V.W.
        • Richmond R.C.
        • Woolf B.A.
        • Davies N.M.
        • Swanson S.A.
        • VanderWeele T.J.
        • et al.
        Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration.
        bmj. 2021; 375
        • Skrivankova V.W.
        • Richmond R.C.
        • Woolf B.A.
        • Yarmolinsky J.
        • Davies N.M.
        • Swanson S.A.
        • et al.
        Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization: the STROBE-MR Statement.
        JAMA. 2021; 326: 1614-1621
        • Pietzner M.
        • Wheeler E.
        • Carrasco-Zanini J.
        • Cortes A.
        • Koprulu M.
        • Worheide M.A.
        • et al.
        Mapping the proteo-genomic convergence of human diseases.
        Science.eabj1541. 2021;
        • Folkersen L.
        • Fauman E.
        • Sabater-Lleal M.
        • Strawbridge R.J.
        • Frånberg M.
        • Sennblad B.
        • et al.
        Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease.
        PLoS genetics. 2017; 13e1006706
        • Yao C.
        • Chen G.
        • Song C.
        • Keefe J.
        • Mendelson M.
        • Huan T.
        • et al.
        Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease.
        Nat Commun. 2018; 9: 3268
        • Sun B.B.
        • Maranville J.C.
        • Peters J.E.
        • Stacey D.
        • Staley J.R.
        • Blackshaw J.
        • et al.
        Genomic atlas of the human plasma proteome.
        Nature. 2018; 558: 73-79
        • Suhre K.
        • Arnold M.
        • Bhagwat A.M.
        • Cotton R.J.
        • Engelke R.
        • Raffler J.
        • et al.
        Connecting genetic risk to disease end points through the human blood plasma proteome.
        Nature communications. 2017; 8: 1-14
        • Emilsson V.
        • Ilkov M.
        • Lamb J.R.
        • Finkel N.
        • Gudmundsson E.F.
        • Pitts R.
        • et al.
        Co-regulatory networks of human serum proteins link genetics to disease.
        Science. 2018; 361: 769-773
        • Zheng J.
        • Haberland V.
        • Baird D.
        • Walker V.
        • Haycock P.C.
        • Hurle M.R.
        • et al.
        Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases.
        Nature genetics. 2020; 52: 1122-1131
        • Chong M.
        • Sjaarda J.
        • Pigeyre M.
        • Mohammadi-Shemirani P.
        • Lali R.
        • Shoamanesh A.
        • et al.
        Novel Drug Targets for Ischemic Stroke Identified Through Mendelian Randomization Analysis of the Blood Proteome.
        Circulation. 2019; 140: 819-830
        • Zhou S.
        • Butler-Laporte G.
        • Nakanishi T.
        • Morrison D.R.
        • Afilalo J.
        • Afilalo M.
        • et al.
        A Neanderthal OAS1 isoform protects individuals of European ancestry against COVID-19 susceptibility and severity.
        Nature medicine. 2021; 27: 659-667
        • Demontis D.
        • Walters R.K.
        • Martin J.
        • Mattheisen M.
        • Als T.D.
        • Agerbo E.
        • et al.
        Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder.
        Nat Genet. 2019; 51: 63-75
        • Watson H.J.
        • Yilmaz Z.
        • Thornton L.M.
        • Hubel C.
        • Coleman J.R.I.
        • Gaspar H.A.
        • et al.
        Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa.
        Nat Genet. 2019; 51: 1207-1214
        • Otowa T.
        • Hek K.
        • Lee M.
        • Byrne E.M.
        • Mirza S.S.
        • Nivard M.G.
        • et al.
        Meta-analysis of genome-wide association studies of anxiety disorders.
        Mol Psychiatry. 2016; 21: 1391-1399
        • Grove J.
        • Ripke S.
        • Als T.D.
        • Mattheisen M.
        • Walters R.K.
        • Won H.
        • et al.
        Identification of common genetic risk variants for autism spectrum disorder.
        Nat Genet. 2019; 51: 431-444
        • Mullins N.
        • Forstner A.J.
        • O'Connell K.S.
        • Coombes B.
        • Coleman J.R.I.
        • Qiao Z.
        • et al.
        Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology.
        Nat Genet. 2021; 53: 817-829
        • Howard D.M.
        • Adams M.J.
        • Clarke T.K.
        • Hafferty J.D.
        • Gibson J.
        • Shirali M.
        • et al.
        Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions.
        Nat Neurosci. 2019; 22: 343-352
        • International Obsessive Compulsive Disorder Foundation Genetics C.
        • Studies O.C.D.C.G.A.
        Revealing the complex genetic architecture of obsessive-compulsive disorder using meta-analysis.
        Mol Psychiatry. 2018; 23: 1181-1188
        • Nievergelt C.M.
        • Maihofer A.X.
        • Klengel T.
        • Atkinson E.G.
        • Chen C.Y.
        • Choi K.W.
        • et al.
        International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci.
        Nat Commun. 2019; 10: 4558
        • Ripke S.
        • Walters J.T.
        • O’Donovan M.C.
        • Consortium SWGotPG.
        Mapping genomic loci prioritises genes and implicates synaptic biology in schizophrenia.
        MedRxiv. 2020;
        • Yu D.
        • Sul J.H.
        • Tsetsos F.
        • Nawaz M.S.
        • Huang A.Y.
        • Zelaya I.
        • et al.
        Interrogating the genetic determinants of Tourette’s syndrome and other tic disorders through genome-wide association studies.
        American Journal of Psychiatry. 2019; 176: 217-227
        • Nikpay M.
        • Goel A.
        • Won H.H.
        • Hall L.M.
        • Willenborg C.
        • Kanoni S.
        • et al.
        A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease.
        Nat Genet. 2015; 47: 1121-1130
        • Machiela M.J.
        • Chanock S.J.
        LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants.
        Bioinformatics. 2015; 31: 3555-3557
        • Hemani G.
        • Zheng J.
        • Elsworth B.
        • Wade K.H.
        • Haberland V.
        • Baird D.
        • et al.
        The MR-Base platform supports systematic causal inference across the human phenome.
        Elife. 2018; 7
        • Bowden J.
        • Davey Smith G.
        • Haycock P.C.
        • Burgess S.
        Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator.
        Genetic epidemiology. 2016; 40: 304-314
        • Davies N.M.
        • Holmes M.V.
        • Smith G.D.
        Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians.
        bmj. 2018; : 362
        • Hemani G.
        • Tilling K.
        • Davey Smith G.
        Orienting the causal relationship between imprecisely measured traits using GWAS summary data.
        PLoS genetics. 2017; 13e1007081
        • Kamat M.A.
        • Blackshaw J.A.
        • Young R.
        • Surendran P.
        • Burgess S.
        • Danesh J.
        • et al.
        PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations.
        Bioinformatics. 2019; 35: 4851-4853
        • Giambartolomei C.
        • Vukcevic D.
        • Schadt E.E.
        • Franke L.
        • Hingorani A.D.
        • Wallace C.
        • et al.
        Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.
        PLoS Genet. 2014; 10e1004383
      1. Yang J, Ferreira T, Morris AP, Medland SE, Genetic Investigation of ATC, Replication DIG, et al. (2012): Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet. 44:369-375, S361-363.

        • Consortium G.T.
        The GTEx Consortium atlas of genetic regulatory effects across human tissues.
        Science. 2020; 369: 1318-1330
        • Sjostedt E.
        • Zhong W.
        • Fagerberg L.
        • Karlsson M.
        • Mitsios N.
        • Adori C.
        • et al.
        An atlas of the protein-coding genes in the human, pig, and mouse brain.
        Science. 2020; 367
        • Wishart D.S.
        • Feunang Y.D.
        • Guo A.C.
        • Lo E.J.
        • Marcu A.
        • Grant J.R.
        • et al.
        DrugBank 5.0: a major update to the DrugBank database for 2018.
        Nucleic Acids Res. 2018; 46: D1074-D1082
        • Greene J.
        • Wang M.
        • Liu Y.E.
        • Raymond L.A.
        • Rosen C.
        • Shi Y.E.
        Molecular cloning and characterization of human tissue inhibitor of metalloproteinase 4.
        J Biol Chem. 1996; 271: 30375-30380
        • Beroun A.
        • Mitra S.
        • Michaluk P.
        • Pijet B.
        • Stefaniuk M.
        • Kaczmarek L.
        MMPs in learning and memory and neuropsychiatric disorders.
        Cell Mol Life Sci. 2019; 76: 3207-3228
        • Lepeta K.
        • Kaczmarek L.
        Matrix Metalloproteinase-9 as a Novel Player in Synaptic Plasticity and Schizophrenia.
        Schizophr Bull. 2015; 41: 1003-1009
        • Murphy G.
        Tissue inhibitors of metalloproteinases.
        Genome biology. 2011; 12: 1-7
        • Chopra K.
        • Baveja A.
        • Kuhad A.
        MMPs: a novel drug target for schizophrenia.
        Expert Opin Ther Targets. 2015; 19: 77-85
        • Wegmann F.
        • Petri B.
        • Khandoga A.G.
        • Moser C.
        • Khandoga A.
        • Volkery S.
        • et al.
        ESAM supports neutrophil extravasation, activation of Rho, and VEGF-induced vascular permeability.
        J Exp Med. 2006; 203: 1671-1677
      2. Pouget JG, Goncalves VF, Schizophrenia Working Group of the Psychiatric Genomics C, Spain SL, Finucane HK, Raychaudhuri S, et al. (2016): Genome-Wide Association Studies Suggest Limited Immune Gene Enrichment in Schizophrenia Compared to 5 Autoimmune Diseases. Schizophr Bull. 42:1176-1184.

        • Nasdala I.
        • Wolburg-Buchholz K.
        • Wolburg H.
        • Kuhn A.
        • Ebnet K.
        • Brachtendorf G.
        • et al.
        A transmembrane tight junction protein selectively expressed on endothelial cells and platelets.
        J Biol Chem. 2002; 277: 16294-16303
        • Xu C.
        • Ng D.T.
        Glycosylation-directed quality control of protein folding.
        Nat Rev Mol Cell Biol. 2015; 16: 742-752
        • Yanagisawa K.
        • Konishi H.
        • Arima C.
        • Tomida S.
        • Takeuchi T.
        • Shimada Y.
        • et al.
        Novel metastasis-related gene CIM functions in the regulation of multiple cellular stress-response pathways.
        Cancer Res. 2010; 70: 9949-9958
        • Howe K.L.
        • Achuthan P.
        • Allen J.
        • Allen J.
        • Alvarez-Jarreta J.
        • Amode M.R.
        • et al.
        Ensembl 2021.
        Nucleic Acids Res. 2021; 49: D884-D891
        • Feczko E.
        • Miranda-Dominguez O.
        • Marr M.
        • Graham A.M.
        • Nigg J.T.
        • Fair D.A.
        The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes.
        Trends Cogn Sci. 2019; 23: 584-601
        • Masi A.
        • DeMayo M.M.
        • Glozier N.
        • Guastella A.J.
        An Overview of Autism Spectrum Disorder, Heterogeneity and Treatment Options.
        Neurosci Bull. 2017; 33: 183-193
        • Faraone S.V.
        • Biederman J.
        • Chen W.J.
        • Milberger S.
        • Warburton R.
        • Tsuang M.T.
        Genetic heterogeneity in attention-deficit hyperactivity disorder (ADHD): gender, psychiatric comorbidity, and maternal ADHD.
        J Abnorm Psychol. 1995; 104: 334-345
        • Martino D.J.
        • Marengo E.
        • Igoa A.
        • Strejilevich S.A.
        Neurocognitive heterogeneity in older adults with bipolar disorders.
        Psychiatry Res. 2018; 262: 510-512
        • Tsuang M.T.
        • Faraone S.V.
        The case for heterogeneity in the etiology of schizophrenia.
        Schizophr Res. 1995; 17: 161-175
        • Cai N.
        • Choi K.W.
        • Fried E.I.
        Reviewing the genetics of heterogeneity in depression: operationalizations, manifestations and etiologies.
        Hum Mol Genet. 2020; 29: R10-R18
        • Ruan S.
        • Zhou Y.
        • Jiang X.
        • Gao H.
        Rethinking CRITID Procedure of Brain Targeting Drug Delivery: Circulation, Blood Brain Barrier Recognition, Intracellular Transport, Diseased Cell Targeting, Internalization, and Drug Release.
        Adv Sci (Weinh). 2021; 82004025
        • Pardridge W.M.
        Blood-brain barrier drug targeting: the future of brain drug development.
        Mol Interv. 2003; 3 (151): 90-105
        • Goodall E.F.
        • Leach V.
        • Wang C.
        • Cooper-Knock J.
        • Heath P.R.
        • Baker D.
        • et al.
        Age-Associated mRNA and miRNA Expression Changes in the Blood-Brain Barrier.
        Int J Mol Sci. 2019; 20
      3. Consortium GT, Laboratory DA, Coordinating Center -Analysis Working G, Statistical Methods groups-Analysis Working G, Enhancing Gg, Fund NIHC, et al. (2017): Genetic effects on gene expression across human tissues. Nature. 550:204-213.

        • Mele M.
        • Ferreira P.G.
        • Reverter F.
        • DeLuca D.S.
        • Monlong J.
        • Sammeth M.
        • et al.
        Human genomics. The human transcriptome across tissues and individuals.
        Science. 2015; 348: 660-665
        • Drechsler C.
        • Bolland M.J.
        • Reid I.
        • Willeit J.
        • Schett G.
        • Santer P.
        • et al.
        Estimating dose-response relationships for vitamin D with coronary heart disease, stroke, and all-cause mortality: observational and Mendelian randomisation analyses.
        The Lancet Diabetes & Endocrinology. 2021;
        • Zheng J.
        • Baird D.
        • Borges M.C.
        • Bowden J.
        • Hemani G.
        • Haycock P.
        • et al.
        Recent Developments in Mendelian Randomization Studies.
        Curr Epidemiol Rep. 2017; 4: 330-345
        • Taylor A.E.
        • Davies N.M.
        • Ware J.J.
        • VanderWeele T.
        • Smith G.D.
        • Munafò M.R.
        Mendelian randomization in health research: using appropriate genetic variants and avoiding biased estimates.
        Economics & Human Biology. 2014; 13: 99-106