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
    Address correspondence to Yu Fang, M.S.E.
    Michigan Neuroscience Institute, University of Michigan, Ann Arbor, Michigan
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  • Lars G. Fritsche
    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
    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
    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
    Department of Psychology, University of Michigan, Ann Arbor, Michigan
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      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.


      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.


      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.


      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.


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      Linked Article

      • Depression Genetics as a Window Into Physical and Mental Health
        Biological PsychiatryVol. 92Issue 12
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          Preventing depression and its concomitant health issues is critical to promote healthy aging and extend the lifespan. It is well known that depression and poor health are intertwined (1), but why they are intertwined remains an area of active inquiry. The idea that depression may share underlying biology—including common genetic mechanisms—with other health conditions is appealing, as knowledge of such mechanisms could inform treatments to ameliorate both. To that end, year upon year we are learning more about the complex genetic architecture of major depression (2), which provides us in theory with increasingly powerful genome-wide tools to study the relationship between depression and health.
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