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Polygenic Liability to Depression Is Associated With Multiple Medical Conditions in the Electronic Health Record: Phenome-wide Association Study of 46,782 Individuals

  • Yu Fang
    Correspondence
    Address correspondence to Yu Fang, M.S.E.
    Affiliations
    Michigan Neuroscience Institute, University of Michigan, Ann Arbor, Michigan
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  • Lars G. Fritsche
    Affiliations
    Department of Biostatistics, School of Public Health, University of Michigan Medicine, Ann Arbor, Michigan

    Rogel Cancer Center, University of Michigan Medicine, Ann Arbor, Michigan

    Center for Statistical Genetics, School of Public Health, University of Michigan Medicine, Ann Arbor, Michigan
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  • Bhramar Mukherjee
    Affiliations
    Department of Biostatistics, School of Public Health, University of Michigan Medicine, Ann Arbor, Michigan

    Rogel Cancer Center, University of Michigan Medicine, Ann Arbor, Michigan

    Center for Statistical Genetics, School of Public Health, University of Michigan Medicine, Ann Arbor, Michigan

    Department of Epidemiology, School of Public Health, University of Michigan Medicine, Ann Arbor, Michigan
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  • Srijan Sen
    Affiliations
    Michigan Neuroscience Institute, University of Michigan, Ann Arbor, Michigan

    Department of Psychiatry, University of Michigan Medicine, Ann Arbor, Michigan
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  • Leah S. Richmond-Rakerd
    Affiliations
    Department of Psychology, University of Michigan, Ann Arbor, Michigan
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      Abstract

      Background

      Major depressive disorder (MDD) is a leading cause of disease-associated disability, with much of the increased burden due to psychiatric and medical comorbidity. This comorbidity partly reflects common genetic influences across conditions. Integrating molecular-genetic tools with health records enables tests of association with the broad range of physiological and clinical phenotypes. However, standard phenome-wide association studies analyze associations with individual genetic variants. For polygenic traits such as MDD, aggregate measures of genetic risk may yield greater insight into associations across the clinical phenome.

      Methods

      We tested for associations between a genome-wide polygenic risk score for MDD and medical and psychiatric traits in a phenome-wide association study of 46,782 unrelated, European-ancestry participants from the Michigan Genomics Initiative.

      Results

      The MDD polygenic risk score was associated with 211 traits from 15 medical and psychiatric disease categories at the phenome-wide significance threshold. After excluding patients with depression, continued associations were observed with respiratory, digestive, neurological, and genitourinary conditions; neoplasms; and mental disorders. Associations with tobacco use disorder, respiratory conditions, and genitourinary conditions persisted after accounting for genetic overlap between depression and other psychiatric traits. Temporal analyses of time-at-first-diagnosis indicated that depression disproportionately preceded chronic pain and substance-related disorders, while asthma disproportionately preceded depression.

      Conclusions

      The present results can inform the biological links between depression and both mental and systemic diseases. Although MDD polygenic risk scores cannot currently forecast health outcomes with precision at the individual level, as molecular-genetic discoveries for depression increase, these tools may augment risk prediction for medical and psychiatric conditions.

      Keywords

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      References

        • Crimmins E.M.
        Recent trends and increasing differences in life expectancy present opportunities for multidisciplinary research on aging.
        Nat Aging. 2021; 1: 12-13
        • GBD 2017 Disease and Injury Incidence and Prevalence Collaborators
        Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017 [published correction appears in Lancet 2019; 393:e44].
        Lancet. 2018; 392: 1789-1858
        • Patten S.B.
        • Williams J.V.A.
        • Lavorato D.H.
        • Modgill G.
        • Jetté N.
        • Eliasziw M.
        Major depression as a risk factor for chronic disease incidence: Longitudinal analyses in a general population cohort.
        Gen Hosp Psychiatry. 2008; 30: 407-413
        • Rotella F.
        • Mannucci E.
        Depression as a risk factor for diabetes: A meta-analysis of longitudinal studies.
        J Clin Psychiatry. 2013; 74: 31-37
        • Scott K.M.
        • Lim C.
        • Al-Hamzawi A.
        • Alonso J.
        • Bruffaerts R.
        • Caldas-de-Almeida J.M.
        • et al.
        Association of mental disorders with subsequent chronic physical conditions: World Mental Health Surveys from 17 countries.
        JAMA Psychiatry. 2016; 73: 150-158
        • Whooley M.A.
        • de Jonge P.
        • Vittinghoff E.
        • Otte C.
        • Moos R.
        • Carney R.M.
        • et al.
        Depressive symptoms, health behaviors, and risk of cardiovascular events in patients with coronary heart disease.
        JAMA. 2008; 300: 2379-2388
        • Kiecolt-Glaser J.K.
        • Glaser R.
        Depression and immune function: Central pathways to morbidity and mortality.
        J Psychosom Res. 2002; 53: 873-876
        • Penninx B.W.J.H.
        Depression and cardiovascular disease: Epidemiological evidence on their linking mechanisms.
        Neurosci Biobehav Rev. 2017; 74: 277-286
        • Archer G.
        • Kuh D.
        • Hotopf M.
        • Stafford M.
        • Richards M.
        Association between lifetime affective symptoms and premature mortality.
        JAMA Psychiatry. 2020; 77: 806-813
        • Han K.M.
        • Kim M.S.
        • Kim A.
        • Paik J.W.
        • Lee J.
        • Ham B.J.
        Chronic medical conditions and metabolic syndrome as risk factors for incidence of major depressive disorder: A longitudinal study based on 4.7 million adults in South Korea.
        J Affect Disord. 2019; 257: 486-494
        • Tang P.L.
        • Wang H.H.
        • Chou F.H.
        A systematic review and meta-analysis of demoralization and depression in patients with cancer.
        Psychosomatics. 2015; 56: 634-643
        • Sullivan P.F.
        • Neale M.C.
        • Kendler K.S.
        Genetic epidemiology of major depression: Review and meta-analysis.
        Am J Psychiatry. 2000; 157: 1552-1562
        • Amare A.T.
        • Schubert K.O.
        • Klingler-Hoffmann M.
        • Cohen-Woods S.
        • Baune B.T.
        The genetic overlap between mood disorders and cardiometabolic diseases: A systematic review of genome wide and candidate gene studies.
        Transl Psychiatry. 2017; 7e1007
        • Kendler K.S.
        • Gardner C.O.
        • Fiske A.
        • Gatz M.
        Major depression and coronary artery disease in the Swedish Twin Registry: Phenotypic, genetic, and environmental sources of comorbidity.
        Arch Gen Psychiatry. 2009; 66: 857-863
        • Scherrer J.F.
        • Xian H.
        • Bucholz K.K.
        • Eisen S.A.
        • Lyons M.J.
        • Goldberg J.
        • et al.
        A twin study of depression symptoms, hypertension, and heart disease in middle-aged men.
        Psychosom Med. 2003; 65: 548-557
        • van Hecke O.
        • Hocking L.J.
        • Torrance N.
        • Campbell A.
        • Padmanabhan S.
        • Porteous D.J.
        • et al.
        Chronic pain, depression and cardiovascular disease linked through a shared genetic predisposition: Analysis of a family-based cohort and twin study.
        PLoS One. 2017; 12e0170653
        • Khandaker G.M.
        • Zuber V.
        • Rees J.M.B.
        • Carvalho L.
        • Mason A.M.
        • Foley C.N.
        • et al.
        Shared mechanisms between coronary heart disease and depression: Findings from a large UK general population-based cohort [published correction appears in Mol Psychiatry 2021; 26:3659–3661].
        Mol Psychiatry. 2020; 25: 1477-1486
        • Wium-Andersen M.K.
        • Villumsen M.D.
        • Wium-Andersen I.K.
        • Jørgensen M.B.
        • Hjelmborg Jvon B.
        • Christensen K.
        • Osler M.
        The familial and genetic contribution to the association between depression and cardiovascular disease: A twin cohort study.
        Mol Psychiatry. 2020; 26: 4245-4253
        • Shen X.
        • Howard D.M.
        • Adams M.J.
        • Hill W.D.
        • Clarke T.-K.
        • Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, et al.
        A phenome-wide association and Mendelian randomisation study of polygenic risk for depression in UK Biobank.
        Nat Commun. 2020; 11: 2301
        • Krapohl E.
        • Euesden J.
        • Zabaneh D.
        • Pingault J.B.
        • Rimfeld K.
        • von Stumm S.
        • et al.
        Phenome-wide analysis of genome-wide polygenic scores.
        Mol Psychiatry. 2016; 21: 1188-1193
        • McCoy T.H.
        • Castro V.M.
        • Snapper L.
        • Hart K.
        • Januzzi J.L.
        • Huffman J.C.
        • Perlis R.H.
        Polygenic loading for major depression is associated with specific medical comorbidity.
        Transl Psychiatry. 2017; 7: e1238
        • Mulugeta A.
        • Zhou A.
        • King C.
        • Hyppönen E.
        Association between major depressive disorder and multiple disease outcomes: A phenome-wide Mendelian randomisation study in the UK Biobank.
        Mol Psychiatry. 2020; 25: 1469-1476
        • Kember R.L.
        • Merikangas A.K.
        • Verma S.S.
        • Verma A.
        • Judy R.
        • Regeneron Genetics Center, et al.
        Polygenic risk of psychiatric disorders exhibits cross-trait associations in electronic health record data from European ancestry individuals.
        Biol Psychiatry. 2021; 89: 236-245
        • Dennis J.
        • Sealock J.
        • Levinson R.T.
        • Farber-Eger E.
        • Franco J.
        • Fong S.
        • et al.
        Genetic risk for major depressive disorder and loneliness in sex-specific associations with coronary artery disease.
        Mol Psychiatry. 2021; 26: 4254-4264
        • Zheutlin A.B.
        • Dennis J.
        • Karlsson Linnér R.
        • Moscati A.
        • Restrepo N.
        • Straub P.
        • et al.
        Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four health care systems.
        Am J Psychiatry. 2019; 176: 846-855
        • Moffitt T.E.
        • Caspi A.
        Psychiatry’s opportunity to prevent the rising burden of age-related disease.
        JAMA Psychiatry. 2019; 76: 461-462
        • 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
        • Fritsche L.G.
        • Gruber S.B.
        • Wu Z.
        • Schmidt E.M.
        • Zawistowski M.
        • Moser S.E.
        • et al.
        Association of polygenic risk scores for multiple cancers in a phenome-wide study: Results from the Michigan Genomics Initiative.
        Am J Hum Genet. 2018; 102: 1048-1061
        • Grotzinger A.D.
        • Mallard T.T.
        • Akingbuwa W.A.
        • Ip H.F.
        • Adams M.J.
        • Lewis C.M.
        • et al.
        Genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic, and molecular genetic levels of analysis.
        Nat Genet. 2022; 54: 548-559
        • Cross-Disorder Group of the Psychiatric Genomics Consortium
        Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders.
        Cell. 2019; 179: 1469-1482.e11
        • Surakka I.
        • Fritsche L.G.
        • Zhou W.
        • Backman J.
        • Kosmicki J.A.
        • Lu H.
        • et al.
        MEPE loss-of-function variant associates with decreased bone mineral density and increased fracture risk.
        Nat Commun. 2020; 11: 4093
        • Zawistowski M.
        • Fritsche L.G.
        • Pandit A.
        • Vanderwerff B.
        • Patil S.
        • Schmidt E.M.
        • et al.
        The Michigan Genomics Initiative: A biobank linking genotypes and electronic clinical records in Michigan Medicine patients.
        medRxiv. 2021; https://doi.org/10.1101/2021.12.15.21267864
        • Fritsche L.G.
        • Beesley L.J.
        • VandeHaar P.
        • Peng R.B.
        • Salvatore M.
        • Zawistowski M.
        • et al.
        Exploring various polygenic risk scores for skin cancer in the phenomes of the Michigan Genomics Initiative and the UK Biobank with a visual catalog: PRSWeb.
        PLoS Genet. 2019; 15e1008202
        • Wang C.
        • Zhan X.
        • Bragg-Gresham J.
        • Kang H.M.
        • Stambolian D.
        • Chew E.Y.
        • et al.
        Ancestry estimation and control of population stratification for sequence-based association studies.
        Nat Genet. 2014; 46: 409-415
        • Li J.Z.
        • Absher D.M.
        • Tang H.
        • Southwick A.M.
        • Casto A.M.
        • Ramachandran S.
        • et al.
        Worldwide human relationships inferred from genome-wide patterns of variation.
        Science. 2008; 319: 1100-1104
        • Manichaikul A.
        • Mychaleckyj J.C.
        • Rich S.S.
        • Daly K.
        • Sale M.
        • Chen W.M.
        Robust relationship inference in genome-wide association studies.
        Bioinformatics. 2010; 26: 2867-2873
        • Abraham K.J.
        • Diaz C.
        Identifying large sets of unrelated individuals and unrelated markers.
        Source Code Biol Med. 2014; 9: 6
        • McCarthy S.
        • Das S.
        • Kretzschmar W.
        • Delaneau O.
        • Wood A.R.
        • Teumer A.
        • et al.
        A reference panel of 64,976 haplotypes for genotype imputation.
        Nat Genet. 2016; 48: 1279-1283
        • Carroll R.J.
        • Bastarache L.
        • Denny J.C.
        R PheWAS: Data analysis and plotting tools for phenome-wide association studies in the R environment.
        Bioinform. 2014; 30: 2375-2376
        • Denny J.C.
        • Bastarache L.
        • Ritchie M.D.
        • Carroll R.J.
        • Zink R.
        • Mosley J.D.
        • et al.
        Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data.
        Nat Biotechnol. 2013; 31: 1102-1110
        • PheWAS Resources
        Phecode Map 1.2 with ICD-9 Codes.
        (Available at:)
        https://phewascatalog.org/phecodes
        Date accessed: March 3, 2022
        • Wei W.Q.
        • Bastarache L.A.
        • Carroll R.J.
        • Marlo J.E.
        • Osterman T.J.
        • Gamazon E.R.
        • et al.
        Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record.
        PLoS One. 2017; 12e0175508
        • Wu P.
        • Gifford A.
        • Meng X.
        • Li X.
        • Campbell H.
        • Varley T.
        • et al.
        Mapping ICD-10 and ICD-10-CM codes to phecodes: Workflow development and initial evaluation.
        JMIR Med Inform. 2019; 7e14325
      1. PheWAS Resources: Phecode Map 1.2 with ICD-10cm Codes (beta).
        (Available at:)
        https://phewascatalog.org/phecodes_icd10cm
        Date accessed: March 3, 2022
        • Bastarache L.
        Using phecodes for research with the electronic health record: From PheWAS to PheRS.
        Annu Rev Biomed Data Sci. 2021; 4: 1-19
        • Ho D.
        • Imai K.
        • King G.
        • Stuart E.A.
        MatchIt: Nonparametric preprocessing for parametric causal inference.
        J Stat Softw. 2011; 42: 1-28
        • Ge T.
        • Chen C.Y.
        • Ni Y.
        • Feng Y.-C.A.
        • Smoller J.W.
        Polygenic prediction via Bayesian regression and continuous shrinkage priors.
        Nat Commun. 2019; 10: 1776
        • Ma Y.
        • Zhou X.
        Genetic prediction of complex traits with polygenic scores: A statistical review.
        Trends Genet. 2021; 37: 995-1011
        • Atlantis E.
        • Fahey P.
        • Cochrane B.
        • Smith S.
        Bidirectional associations between clinically relevant depression or anxiety and COPD: A systematic review and meta-analysis.
        Chest. 2013; 144: 766-777
        • Richmond-Rakerd L.S.
        • D’Souza S.
        • Milne B.J.
        • Caspi A.
        • Moffitt T.E.
        Longitudinal associations of mental disorders with physical diseases and mortality among 2.3 million New Zealand citizens.
        JAMA Netw Open. 2021; 4e2033448
        • Carvalho A.F.
        • Sharma M.S.
        • Brunoni A.R.
        • Vieta E.
        • Fava G.A.
        The safety, tolerability and risks associated with the use of newer generation antidepressant drugs: A critical review of the literature.
        Psychother Psychosom. 2016; 85: 270-288
        • Trinchieri M.
        • Perletti G.
        • Magri V.
        • Stamatiou K.
        • Montanari E.
        • Trinchieri A.
        Urinary side effects of psychotropic drugs: A systematic review and metanalysis.
        Neurourol Urodyn. 2021; 40: 1333-1348
        • Kendler K.S.
        • Aggen S.H.
        • Knudsen G.P.
        • Røysamb E.
        • Neale M.C.
        • Reichborn-Kjennerud T.
        The structure of genetic and environmental risk factors for syndromal and subsyndromal common DSM-IV axis I and all axis II disorders.
        Am J Psychiatry. 2011; 168: 29-39
        • Smoller J.W.
        • Andreassen O.A.
        • Edenberg H.J.
        • Faraone S.V.
        • Glatt S.J.
        • Kendler K.S.
        Psychiatric genetics and the structure of psychopathology [published correction appears in Mol Psychiatry 2019; 24:471.
        Mol Psychiatry. 2019; 24: 409-420
        • Anttila V.
        • Bulik-Sullivan B.
        • Finucane H.K.
        • Walters R.K.
        • Bras J.
        • et al.
        • Brain Consortium
        Analysis of shared heritability in common disorders of the brain.
        Science. 2018; 360 (6395):eaap8757.
        • Jia Y.
        • Li F.
        • Liu Y.F.
        • Zhao J.P.
        • Leng M.M.
        • Chen L.
        Depression and cancer risk: A systematic review and meta-analysis.
        Public Health. 2017; 149: 138-148
        • Wang Y.H.
        • Li J.Q.
        • Shi J.F.
        • Que J.Y.
        • Liu J.J.
        • Lappin J.M.
        • et al.
        Depression and anxiety in relation to cancer incidence and mortality: A systematic review and meta-analysis of cohort studies.
        Mol Psychiatry. 2020; 25: 1487-1499
        • Lanni C.
        • Masi M.
        • Racchi M.
        • Govoni S.
        Cancer and Alzheimer’s disease inverse relationship: An age-associated diverging derailment of shared pathways.
        Mol Psychiatry. 2021; 26: 280-295
        • Ware E.B.
        • Schmitz L.L.
        • Faul J.
        • Gard A.
        • Mitchell C.
        • Smith J.A.
        • et al.
        Heterogeneity in polygenic scores for common human traits.
        bioRxiv. 2017; https://doi.org/10.1101/106062
        • Belsky D.W.
        • Harden K.P.
        Phenotypic annotation: Using polygenic scores to translate discoveries from genome-wide association studies from the top down.
        Curr Dir Psychol Sci. 2019; 28: 82-90