Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia, Bipolar Disorder, and Depression Among Adults in the US Veterans Affairs Health Care System

Tim B Bigdeli; Georgios Voloudakis; Peter B Barr; Bryan R Gorman; Giulio Genovese; Roseann E Peterson; David E Burstein; Vlad I Velicu; Yuli Li; Rishab Gupta; Manuel Mattheisen; Simone Tomasi; Nallakkandi Rajeevan; Frederick Sayward; Krishnan Radhakrishnan; Sundar Natarajan; Anil K Malhotra; Yunling Shi; Hongyu Zhao; Thomas R Kosten; John Concato; Timothy J O’Leary; Ronald Przygodzki; Theresa Gleason; Saiju Pyarajan; Mary Brophy; Grant D Huang; Sumitra Muralidhar; J Michael Gaziano; Mihaela Aslan; Ayman H Fanous; Philip D Harvey; Panos Roussos

doi:10.1001/jamapsychiatry.2022.2742

. 2022 Sep 14;79(11):1092–1101. doi: 10.1001/jamapsychiatry.2022.2742

Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia, Bipolar Disorder, and Depression Among Adults in the US Veterans Affairs Health Care System

Tim B Bigdeli ^1,^2,^3,^✉, Georgios Voloudakis ^4,^5,⁶, Peter B Barr ^1,^2,³, Bryan R Gorman ⁷, Giulio Genovese ^8,⁹, Roseann E Peterson ^1,^2,^3,¹⁰, David E Burstein ^4,^5,⁶, Vlad I Velicu ^4,^5,⁶, Yuli Li ^11,¹², Rishab Gupta ¹³, Manuel Mattheisen ^14,^15,¹⁶, Simone Tomasi ^4,^5,⁶, Nallakkandi Rajeevan ^11,¹², Frederick Sayward ^11,¹², Krishnan Radhakrishnan ¹⁷, Sundar Natarajan ¹, Anil K Malhotra ^18,^19,²⁰, Yunling Shi ⁷, Hongyu Zhao ^11,¹², Thomas R Kosten ^21,²², John Concato ^12,²³, Timothy J O’Leary ²⁴, Ronald Przygodzki ²⁴, Theresa Gleason ²⁴, Saiju Pyarajan ⁷, Mary Brophy ^7,²⁵, Grant D Huang ²⁴, Sumitra Muralidhar ²⁴, J Michael Gaziano ^7,⁹, Mihaela Aslan ^11,¹², Ayman H Fanous ^26,²⁷, Philip D Harvey ^28,²⁹, Panos Roussos ^4,^5,⁶, for the Cooperative Studies Program (CSP) #572 and Million Veteran Program (MVP)

¹VA New York Harbor Healthcare System, Brooklyn

²Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, New York

³Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, New York

⁴James J. Peters Veterans Affairs Medical Center, Bronx, New York

⁵Departments of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York

⁶Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York

⁷Massachusetts Area Veterans Epidemiology, Research, and Information Center (MAVERIC), Jamaica Plain

⁸Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts

⁹Harvard Medical School, Boston, Massachusetts

¹⁰Department of Psychiatry, Virginia Commonwealth University, Richmond

¹¹Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven, Connecticut

¹²Yale University School of Medicine, New Haven, Connecticut

¹³Department of Psychiatry, Brigham & Women’s Hospital, Harvard Medical School, Boston, Massachusetts

¹⁴Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada

¹⁵Department of Community Health, Dalhousie University, Halifax, Nova Scotia, Canada

¹⁶Department of Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada

¹⁷National Mental Health and Substance Use Policy Laboratory, Substance Abuse and Mental Health Services Administration, Rockville, Maryland

¹⁸Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York

¹⁹Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, New York

²⁰Department of Psychiatry, Hofstra Northwell School of Medicine, Hempstead, New York

²¹Michael E. DeBakey VA Medical Center, Houston, Texas

²²Department of Psychiatry, Neuroscience, Pharmacology, and Immunology and Rheumatology, Baylor College of Medicine, Houston, Texas

²³Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland

²⁴Office of Research and Development, Veterans Health Administration, Washington, DC

²⁵Boston University School of Medicine, Boston, Massachusetts

²⁶Department of Psychiatry, University of Arizona College of Medicine-Phoenix, Phoenix

²⁷Carl T. Hayden Veterans Affairs Medical Center, Phoenix, Arizona

²⁸Bruce W. Carter Miami Veterans Affairs (VA) Medical Center, Miami, Florida

²⁹University of Miami Miller School of Medicine, Miami, Florida

Group Information: The Cooperative Studies Program (CSP) #572 and Million Veteran Program (MVP) investigators are listed in Supplement 2.

Accepted for Publication: July 20, 2022.

Published Online: September 14, 2022. doi:10.1001/jamapsychiatry.2022.2742

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Corresponding Author: Tim B. Bigdeli, PhD, Department of Psychiatry and Behavioral Sciences, Institute for Genomics in Health (IGH), SUNY Downstate Health Sciences University, 450 Clarkson Ave, MSC 1203, Brooklyn, NY 11203 (tim.bigdeli@downstate.edu).

Author Contributions: Drs Bigdeli and Voloudakis had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Bigdeli and Voloudakis jointly contributed to this work. Drs Harvey and Roussos jointly supervised this work.

Concept and design: Bigdeli, Voloudakis, Barr, Gorman, Genovese, Concato, O'Leary, Przygodzki, Gleason, Brophy, Huang, Aslan, Fanous, Harvey, Roussos.

Acquisition, analysis, or interpretation of data: Bigdeli, Voloudakis, Barr, Gorman, Peterson, Burstein, Velicu, Li, Gupta, Mattheisen, Tomasi, Rajeevan, Sayward, Radhakrishnan, Natarajan, Malhotra, Shi, Zhao, Kosten, Concato, Pyarajan, Muralidhar, Gaziano, Aslan, Fanous, Harvey, Roussos.

Drafting of the manuscript: Bigdeli, Voloudakis, Barr, Gorman, Burstein, Kosten, Harvey, Roussos.

Critical revision of the manuscript for important intellectual content: Bigdeli, Voloudakis, Barr, Gorman, Genovese, Peterson, Velicu, Li, Gupta, Mattheisen, Tomasi, Rajeevan, Sayward, Radhakrishnan, Natarajan, Malhotra, Shi, Zhao, Kosten, Concato, O'Leary, Przygodzki, Gleason, Pyarajan, Brophy, Huang, Muralidhar, Gaziano, Aslan, Fanous, Harvey.

Statistical analysis: Bigdeli, Voloudakis, Barr, Gorman, Peterson, Burstein, Li, Rajeevan, Sayward, Shi, Zhao, Fanous, Roussos.

Obtained funding: Kosten, Concato, Przygodzki, Pyarajan, Muralidhar, Gaziano, Aslan, Harvey, Roussos.

Administrative, technical, or material support: Bigdeli, Voloudakis, Peterson, Burstein, Mattheisen, Radhakrishnan, Kosten, Concato, O'Leary, Przygodzki, Gleason, Brophy, Huang, Muralidhar, Gaziano, Aslan, Harvey, Roussos.

Supervision: Bigdeli, Voloudakis, Genovese, Natarajan, Concato, O'Leary, Pyarajan, Huang, Muralidhar, Gaziano, Aslan, Fanous, Harvey, Roussos.

Conflict of Interest Disclosures: Dr Malhotra reported receiving consulting fees from Janssen Pharmaceuticals, Acadia Pharmaceuticals, Informed DNA, and Genomind outside the submitted work. Dr Harvey reported personal fees from Alkermes, Karuna Pharma, Merck Pharma, Minerva Pharma, and Sunovion Pharma outside the submitted work; and royalties from WCG Verasci outside the submitted work. No other disclosures were reported.

Funding/Support: This research was supported by the Department of Veterans Affairs CSP #572 and the Million Veteran Program (MVP-000 and MVP-006). The MVP is supported by the Office of Research and Development, Department of Veterans Affairs. This study was supported by the Veterans Affairs Merit grants (grants BX002395 and BX004189; Dr Roussos). This study was also supported by the National Institutes of Health (grants R01MH125246 to Drs Bigdeli and Roussos; R01MH104964 to Drs Bigdeli and Fanous; K08MH122911 to Dr Voloudakis; R01AG067025 to Dr Roussos; U01MH116442 to Dr Roussos; R01MH109677 to Dr Roussos). This study has also been funded in part by the Brain & Behavior Research Foundation via the 2019 NARSAD Young Investigator Grant (grant 28276 to Dr Bigdeli) and 2020 NARSAD Young Investigator Grant (grant 29350 to Dr Voloudakis). Dr Voloudakis reported grants from the National Institutes of Health and Brain & Behavior Research Foundation during the conduct of the study. Drs Burstein, Pyarajan, Roussos, and Harvey reported support from Merit grants from the Department of Veterans Affairs.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation of the manuscript; and decision to submit the manuscript for publication. The MVP Publication Committee and CSP #572 Executive Committee reviewed and approved the manuscript.

Disclaimer: The contents do not represent the views of the US Department of Veterans Affairs or the US government.

Group Information: The Cooperative Studies Program (CSP) #572 and Million Veteran Program (MVP) investigators appear in Supplement 2.

Additional Contributions: We are grateful to R. Karlsson Linnér, PhD (Leiden University), and T.T. Mallard, PhD (Massachusetts General Hospital), for sharing their script for plotting PheWAS results (eFigure 4 in Supplement 1), which served as the basis of the figure presented herein. Compensation was not received.

Additional Information: Larry J. Siever, original co–principal investigator for CSP #572, passed away in February 2021.

^✉

Corresponding author.

PMCID: PMC9475441 PMID: 36103194

This cross-sectional study benchmarks the penetrance of current neuropsychiatric polygenic risk scores in the Veterans Health Administration health care system, and explores associations between polygenic risk scores and broad categories of human disease via phenome-wide association studies.

Key Points

Question

What is the penetrance of polygenic risk scores (PRSs) for schizophrenia, bipolar disorder, and major depression among US veterans who use the Veterans Health Administration health care system and what health problems are associated with having a higher polygenic burden?

Findings

In this cross-sectional study of 707 299 individuals, PRSs were associated with having ever received a relevant psychiatric diagnosis and were enriched among more frequently hospitalized patients. Higher PRSs were associated with increased odds for numerous mental and physical health diagnoses, even among individuals who lack a formal diagnosis.

Meaning

Individual-level PRSs informed by large-scale genetic studies are portable across US health care systems and have emergent potential for risk stratification, albeit with disparate specificity across ancestries.

Abstract

Importance

Serious mental illnesses, including schizophrenia, bipolar disorder, and depression, are heritable, highly multifactorial disorders and major causes of disability worldwide.

Objective

To benchmark the penetrance of current neuropsychiatric polygenic risk scores (PRSs) in the Veterans Health Administration health care system and to explore associations between PRS and broad categories of human disease via phenome-wide association studies.

Design, Setting, and Participants

Extensive Veterans Health Administration’s electronic health records were assessed from October 1999 to January 2021, and an embedded cohort of 9378 individuals with confirmed diagnoses of schizophrenia or bipolar 1 disorder were found. The performance of schizophrenia, bipolar disorder, and major depression PRSs were compared in participants of African or European ancestry in the Million Veteran Program (approximately 400 000 individuals), and associations between PRSs and 1650 disease categories based on ICD-9/10 billing codes were explored. Last, genomic structural equation modeling was applied to derive novel PRSs indexing common and disorder-specific genetic factors. Analysis took place from January 2021 to January 2022.

Main Outcomes and Measures

Diagnoses based on in-person structured clinical interviews were compared with ICD-9/10 billing codes. PRSs were constructed using summary statistics from genome-wide association studies of schizophrenia, bipolar disorder, and major depression.

Results

Of 707 299 enrolled study participants, 459 667 were genotyped at the time of writing; 84 806 were of broadly African ancestry (mean [SD] age, 58 [12.1] years) and 314 909 were of broadly European ancestry (mean [SD] age, 66.4 [13.5] years). Among 9378 individuals with confirmed diagnoses of schizophrenia or bipolar 1 disorder, 8962 (95.6%) were correctly identified using ICD-9/10 codes (2 or more). Among those of European ancestry, PRSs were robustly associated with having received a diagnosis of schizophrenia (odds ratio [OR], 1.81 [95% CI, 1.76-1.87]; P < 10⁻²⁵⁷) or bipolar disorder (OR, 1.42 [95% CI, 1.39-1.44]; P < 10⁻²⁹⁵). Corresponding effect sizes in participants of African ancestry were considerably smaller for schizophrenia (OR, 1.35 [95% CI, 1.29-1.42]; P < 10⁻³⁸) and bipolar disorder (OR, 1.16 [95% CI, 1.11-1.12]; P < 10⁻¹⁰). Neuropsychiatric PRSs were associated with increased risk for a range of psychiatric and physical health problems.

Conclusions and Relevance

Using diagnoses confirmed by in-person structured clinical interviews and current neuropsychiatric PRSs, the validity of an electronic health records–based phenotyping approach in US veterans was demonstrated, highlighting the potential of PRSs for disentangling biological and mediated pleiotropy.

Introduction

Serious mental illnesses such as schizophrenia, bipolar disorder, and major depression are leading causes of disability and public health expenditure, and affected persons disproportionately experience increased morbidity and early mortality. Recent years have seen important advances in our understanding of the complex multifactorial underpinnings of serious mental illnesses, with genome-wide association studies (GWAS) yielding robust and replicable associations with specific loci (270, 64, and 44 for schizophrenia, bipolar disorder, and major depression, respectively).^1,2,3 However, small effect sizes at individual variants and extreme polygenicity have thwarted the transformative mechanistic insights needed for development of novel therapeutics and prevention strategies.

Polygenic risk scores (PRSs) aggregate genetic associations across the genome, including many variants that do not attain genome-wide significance and can account for more variance in liability than genome-wide significant findings alone, albeit they are typically less predictive than a positive family history⁴ or certain rare copy number variants.⁵ Ever-increasing GWAS sample sizes have seen the variance in liability captured by PRSs climb steadily, from 3% in the first demonstrative application to schizophrenia⁶ to upwards of 10% in recent Psychiatric Genomics Consortium analyses.¹ As applied to nonpsychiatric traits, the clinical utility of PRSs is emergent,⁷ and potential applications in psychiatry are actively being explored,⁸ including risk stratification and predicting treatment response.

With large biobanks now linking the electronic health records (EHRs) of hundreds of thousands of patients to their individual-level genomic data, there are opportunities to explore the associations of PRSs (or specific variants) with a wide range of clinical phenotypes, ie, a genotype-to-phenotype or reverse genetics paradigm. Also known as phenome-wide association studies (PheWAS),⁹ this unbiased, disease-agnostic approach has the potential to uncover hitherto unrecognized associations between distinct diagnostic entities and to aid in disentangling complex pleiotropic associations. A recent application of PheWAS from the PsycheMERGE Consortium analyzed schizophrenia PRSs in more than 100 000 patients from 4 large health care systems (Geisinger Health System, Mount Sinai Health System, Partners HealthCare System, and Vanderbilt University Medical Center) and uncovered robust associations with both psychiatric and nonpsychiatric diagnoses.¹⁰

The Veterans Health Administration (VHA) is the largest integrated health care system in the United States, with 171 medical centers and 1112 outpatient clinics serving more than 9 million veterans. Launched in 2010, the Million Veteran Program (MVP) is a landmark endeavor that links genomic laboratory testing, survey-based self-report data, and EHRs spanning decades, with the goal of creating a mega-biobank and novel evidence base for precision medicine initiatives.¹¹ Demographically and clinically, the 850 000 enrolled participants reflect the population that uses the VHA, with overrepresentation of older individuals and male individuals, as well as higher rates of multiple, chronic conditions compared with the general population,^12,13 despite better access to health care.

Cooperative Studies Program (CSP) #572¹⁴ is a cohort of approximately 9300 veterans with schizophrenia or bipolar 1 disorder who received detailed in-person assessments of clinical diagnosis, functioning, and symptomatology. Within this companion study to the MVP, we evaluated the sensitivity of ICD-9/10 billing codes for schizophrenia, bipolar disorder, and major depression, applying case-control definitions of varying stringency and breadth of clinical phenotype (eg, schizophrenia vs any psychosis). We benchmarked the penetrance of current neuropsychiatric PRSs for schizophrenia,¹ bipolar disorder,² and major depression³ in 400 000 ancestrally diverse MVP participants and explored the broad associations of PRS with physical and mental health conditions via PheWAS. Finally, recognizing the considerable shared genetic basis of these disorders, we applied genomic structural equation modeling to derive common and disorder-specific latent genetic factors for comparative genomic analyses and explored pleiotropic associations of these latent factors with PheWAS.

Methods

Study Participants

This study was approved by the Veterans Affairs (VA) Central Institutional Review Board, and all patients provided written informed consent. Additional details of study ascertainment and assessment are described elsewhere.¹⁴

CSP #572

Participants were recruited through their clinicians, posted notices at participating VA hospitals, and through word of mouth from January 2011 to January 2020. All patients received the Structured Clinical Interview for the DSM¹⁵ and met lifetime DSM-IV criteria for schizophrenia (n = 3953) or bipolar 1 disorder (n = 5425). Patients with major neurologic illnesses or medical problems that could interfere with central nervous system function were excluded. Information from medical records, patients’ clinicians, or other informants were used, if needed, to confirm diagnoses. Diagnosed substance misuse was not an exclusion criterion, given some concerns about representativeness. Participants received a brief cognitive assessment and the University of California, San Diego, Performance-Based Skills Assessment, Brief version, a performance-based measure of everyday functional skills.¹⁶

MVP

Participants were active users of the VHA health care system and were recruited through invitational mailings or by MVP staff while receiving clinical care. Informed consent and authorization per the Health Insurance Portability and Accountability Act were the only other inclusion criteria. Participants were recruited from January 2011 to January 2020. All participants completed a baseline survey, which includes information on demographic factors, health status, lifestyle habits, military experiences, medical history, and family history of specific illnesses and physical features; many also completed an optional lifestyle survey.¹¹

At the time of manuscript submission, 4697 individuals (approximately 50% of CSP #572 and approximately 0.7% of MVP) were dually enrolled in both CSP #572 and MVP. CSP #572 and MVP participants were genotyped on the MVP 1.0 Axiom array¹⁷ (eMethods in Supplement 1). Participants were classified as being of African or European ancestry using the harmonized ancestry and race and ethnicity method,¹⁸ which combines information on genetic ancestry with self-identified race and ethnicity.

EHRs

For 9378 CSP #572 participants and 697 921 nonoverlapping MVP enrollees, we extracted ICD-9/10 billing codes related to schizophrenia, bipolar disorder, and major depression (eTable 1 in Supplement 1) and prescription records for commonly prescribed antipsychotics, mood stabilizers, and antidepressants (eTable 2 in Supplement 1) from the VHA Corporate Data Warehouse.

We compared CSP #572 participants’ Structured Clinical Interview for the DSM–confirmed diagnoses with the ICD-9/10 codes recorded in their EHRs. Given the challenge of the differential diagnosis, for individuals with both schizophrenia and bipolar disorder codes, we took the mode of the 5 most recent entries as the prevailing diagnosis.

PRS Profiling

We constructed PRSs from published Psychiatric Genomics Consortium GWAS results,^1,2,3 testing these for association with disease outcomes in the MVP cohort. Variants that met quality control filtering in both the training and target data sets were clumped in the appropriate 1000 Genomes Project phase 3 population (r² > 0.1; 500-kb window), excluding the major histocompatibility complex. For varying P value thresholds in each training data set (eMethods in Supplement 1), scores were constructed by summing the number of tested alleles weighted by their effect estimates (ie, the log of the allelic odds ratio). To better facilitate comparison of our results with those based on civilian cohorts, we used a similar approach to Zheutlin et al,¹⁰ including comparing results based on a recently developed bayesian framework that applies continuous shrinkage to test statistics, PRS–continuous shrinkage.¹⁹

Genomic Structural Equation Modeling

We used genomic structural equation modeling²⁰ to model the genetic covariance structure underlying schizophrenia, bipolar disorder, and major depression. Briefly, genomic structural equation modeling models the multivariate genetic architecture of complex traits by estimating individual single-nucleotide variants (SNVs; formerly, single-nucleotide polymorphisms or SNPs) associations on latent constructs, is robust to sample overlap and sample-size imbalance, and does not require individual-level genotypes. We estimated the SNV associations with a common factor (shared across disorders) as well as associations specific to each disorder (eMethods in Supplement 1).

PheWAS

We used PheWAS to explore the associations between neuropsychiatric PRSs and phecodes representing groupings of associated ICD-9/10 billing codes.⁹ When testing individual phecodes, we required individuals with these disorders and controls to have 2 or more and zero codes, respectively. We applied logistic regression to test scaled PRSs (mean [SD], 0 [1]) for association with phecodes within ancestry groups, covarying for age, age², sex, and 6 ancestry principal components.

We performed a series of sensitivity analyses, covarying for selected diagnoses or treatment with antipsychotics, mood stabilizers, and antidepressants, or removing individuals with any lifetime diagnosis of psychotic, mood, or substance disorders (eMethods in Supplement 1). Two-sided P values were statistically significant at 10⁻⁵. Analysis took place from January 2021 to January 2022.

Results

Participants

Of 707 299 enrolled study participants, 459 667 were genotyped at the time of writing; 84 806 were of broadly African ancestry (mean [SD] age, 58 [12.1] years), and 314 909 were of broadly European ancestry (mean [SD] age, 66.4 [13.5] years). Of 9378 individuals in CSP #572, 3953 (42%) had schizophrenia (median [SD] age, 56 [10.1] years; 289 [7.3%] female) and 5425 (58%) had bipolar 1 disorder (median [SD] age, 53 [11.5] years; 1005 [18.5%] female). There were 697 921 individuals in MVP (median [SD] age, 61 [14.2] years; 62 749 [9.0%] female). The Table and eTable 3 in Supplement 1 include descriptives for CSP #572 and MVP and display the number of participants meeting various case inclusion criteria based on phecodes and medications.

Table. Cooperative Studies Program (CSP) #572 and Million Veteran Program (MVP) Participants Meeting Varying Electronic Health Records–Based Criteria for Schizophrenia, Bipolar Disorder, and Major Depression .

Diagnosis	Phecode	CSP #572						MVP^a
		Schizophrenia			Bipolar 1 disorder			≥1 ICD-9/10	≥2 ICD-9/10	Inpatient
		≥1 ICD-9/10	≥2 ICD-9/10	Inpatient	≥1 ICD-9/10	≥2 ICD-9/10	Inpatient	≥1 ICD-9/10	≥2 ICD-9/10	Inpatient
Schizophrenia	295.1	3803 (96.2)	3770 (95.4)	2978 (75.3)	1821 (33.6)	1368 (25.2)	848 (15.6)	24 698 (3.5)	18 023 (2.6)	11 532 (1.7)
Paranoid	295.2	+8 (0.2)	+11 (0.3)	+9 (0.2)	+132 (2.4)	+99 (1.8)	+60 (1.1)	+4787 (0.69)	+2806 (0.4)	+1388 (0.2)
Psychosis	295.3	+26 (0.66)	+39 (1.0)	+64 (1.6)	+524 (9.7)	+402 (7.4)	+208 (3.8)	+19 905 (2.9)	+11 200 (1.6)	+5402 (0.8)
Total	NA	3837 (97.1)	3820 (96.6)	3051 (77.2)	2477 (45.7)	1869 (34.5)	1116 (20.6)	49 390 (7.1)	32 029 (4.6)	18 322 (2.6)
Bipolar disorder (mania)	296.1	610 (15.4)	462 (11.7)	189 (4.8)	3381 (62.3)	3056 (56.3)	1134 (20.9)	16 819 (2.4)	10 432 (1.5)	3919 (0.6)
Bipolar disorder (any)	296.1	+1063 (26.9)	691 (17.5)	+613 (15.5)	+1858 (34.2)	+2136 (39.4)	+2660 (49.0)	+62 394 (8.9)	+43 343 (6.2)	+20 228 (2.9)
Total	NA	1673 (42.3)	1153 (29.2)	802 (20.3)	5239 (96.6)	5192 (95.7)	3794 (69.9)	79 213 (11.3)	53 775 (7.7)	24 147 (3.5)
Depression	296.2	2997 (75.8)	2506 (63.4)	1529 (38.7)	4490 (82.8)	4048 (74.6)	2139 (39.4)	332 104 (47.6)	288 561 (41.3)	120 713 (17.3)
Treatment		≥1 Rx	≥2 Trials		≥1 Rx	≥2 Trials		≥1 Rx	≥2 Trials
Antipsychotics		3784 (95.7)	3413 (86.3)		4793 (88.4)	3816 (70.3)		121 339 (17.4)	56 276 (8.1)
Mood stabilizers		1834 (46.4)	706 (17.9)		4854 (89.5)	3319 (61.2)		102 498 (14.7)	33 340 (4.8)
Antidepressants		2928 (74.1)	1921 (48.6)		4529 (83.5)	3446 (63.5)		357 795 (51.3)	218 864 (31.4)

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Abbreviations: NA, not applicable; Rx, prescription.

^{^a}

Participants dually enrolled in CSP #572 and MVP were excluded.

Validation of EHR-Derived Phenotypes

We first sought to evaluate the precision and accuracy of EHR-derived phenotypes to capture caseness based on the Structured Clinical Interview for the DSM–based diagnoses available in CSP #572. Among 9378 patients, 8962 (95.6%) were correctly assigned using 2 or more relevant phecodes.

Overall, 1153 of 3953 confirmed patients with schizophrenia (29.2%) had 2 or more bipolar disorder–related phecodes, and 1869 of 5425 confirmed patients with bipolar 1 disorder (34.5%) had multiple codes for schizophrenia. Taking the prevailing diagnosis resulted in 111 of 3953 individuals with schizophrenia (2.8%) and 507 of 5425 (9.3%) with bipolar 1 disorder being misclassified. For most individuals with misclassified bipolar 1 disorder, the prevailing diagnosis was schizoaffective disorder; these patients were more often of African ancestry (odds ratio [OR], 2.24 [95% CI, 1.83-2.74]; P < 10⁻¹⁴), male (OR, 1.60 [95% CI, 1.22-2.11]; P < .01), and had lower University of California, San Diego, Performance-Based Skills Assessment, Brief version scores (β = –0.09 [95% CI, –0.13 to –0.06]; P < 10⁻⁶) (eTables 4-5 in Supplement 1).

Comparing receiver operating characteristic curves for predictive models based on the varying criteria displayed in the Table, we concluded that a minimum of 2 phecodes offered the best overall balance of sensitivity vs specificity (Figure 1; eTable 6 in Supplement 1).

Figure 1. — For varying schizophrenia (A) and bipolar 1 disorder (B) case criteria displayed in the Table, sensitivity and specificity estimates for a split-half cross-validation experiment are displayed. In each panel, the dashed line indicates a random (50/50) prediction.

Penetrance of Neuropsychiatric PRS in the VA Health Care System

Benchmarking results for neuropsychiatric PRSs based on varying P value thresholds are given in eTables 7 to 9 in Supplement 1. Case prevalence estimates for each decile of PRS, representing the absolute disease prevalence, are displayed in Figure 2. As expected, the prevalence of serious mental illnesses was higher among veterans treated at VHA facilities than in the general population.^21,22

Figure 2. — For PRSs constructed from current Psychiatric Genomics Consortium summary statistics for schizophrenia, bipolar disorder, and major depression, the estimated prevalence of that diagnosis in each PRS decile is displayed separately for populations of African and European ancestries. Ascertained patients enrolled in Cooperative Studies Program #572 were excluded.

The prevalence of schizophrenia among participants of European ancestry in the top and bottom deciles of schizophrenia PRS was 4% and 0.9%, respectively, corresponding to 4.8-fold higher odds (95% CI, 4.22-5.43; P < 10⁻¹³¹). Individuals in the top decile had approximately 2.4-fold higher odds of being diagnosed with schizophrenia than those below the 90% percentile (95% CI, 2.26-2.55; P < 10⁻¹⁸³); 2-fold higher odds for psychosis (phecode 295.3) (95% CI, 1.85-2.09; P < 10⁻¹⁰¹); 1.6-fold higher odds for bipolar disorder (95% CI, 1.53-1.66; P < 10⁻¹⁰⁶); and 1.2-fold (95% CI, 1.15-1.21; P < 10⁻³⁵) for major depression.

Prevalence of bipolar disorder in the top and bottom bipolar disorder PRS deciles were 8.7% and 3.7%, equivalent to a 2.5-fold (95% CI, 2.34-2.71; P < 10⁻¹³⁵) increase in risk. Compared with the bottom 90%, individuals in the top 10% had 1.7 times (95% CI, 1.61-1.75; P < 10⁻¹²⁶) higher likelihood to be diagnosed with bipolar disorder.

Prevalence of major depression were 45% in the top PRS decile and 34% in the bottom decile, corresponding to 1.7-fold difference in risk (95% CI, 1.55-1.94; P < 10⁻²¹). Comparing the top 10% with the individuals in the remaining 90%, we observed an approximately 1.4-fold increase in risk (95% CI, 1.26-1.45; P < 10⁻¹⁶).

Cross-Ancestry Portability of Neuropsychiatric PRS

The higher prevalence of schizophrenia and major depression among individuals of African ancestry was largely not associated with individuals’ risk strata (Figure 2) and as high as 6.8% and 51%, respectively, in the top 10%. In contrast, across bipolar disorder PRS deciles, the absolute prevalence of bipolar disorder was between 5.8% and 6.8%.

Individuals of African ancestry in the top decile of schizophrenia PRSs had approximately 1.4-fold higher risk of diagnoses of schizophrenia than those below the 90% percentile (95% CI, 1.34-1.57; P < 10⁻¹⁸) and 2.2-fold higher risk than those in bottom decile (95% CI, 1.89-2.65; P < 10⁻¹⁹). At extremes, odds of bipolar disorder and major depression were increased 1.6-fold (95% CI, 1.33-1.84; P < 10⁻⁷) and 1.3-fold (95% CI, 1.17-1.39; P < 10⁻⁷), respectively.

Schizophrenia PRS Have Equivalent Relative Penetrance in Civilian and Veteran Health Care Systems

The demographics of the US veteran population differ from cohorts recruited from civilian health care systems. For instance, the VA population is composed of mostly male individuals (approximately 90%) and has higher prevalence of neuropsychiatric illnesses.²³ Using the same training GWAS and bayesian PRS¹⁹ approach as used by the PsycheMERGE consortium,¹⁰ we see a robust association of schizophrenia PRSs with schizophrenia diagnosis (OR per SD-unit increase, 1.56 [95% CI, 1.52-1.61]; P = 2.7 × 10⁻²²²) in the European ancestry subset of our cohort, which is within the confidence interval estimated in the PsycheMERGE study (OR per SD-unit increase, 1.55 [95% CI, 1.39-1.72]).¹⁰ That is, despite stark differences in absolute prevalence between these cohorts, estimates of relative risks did not differ substantively.

Higher Loadings of Neuropsychiatric PRS in More Chronic Illness

We observed a trend of increased polygenic loading in more chronic illness presentations.^1,24 Patients who received inpatient treatment for schizophrenia had significantly higher PRS than those who did not (OR per SD-unit increase, 1.25 [95% CI, 1.18-1.34]; P < 10⁻¹¹); among these individuals, schizophrenia PRS was positively associated with number of hospitalizations (β = 0.198 [95% CI, 0.11-0.28]; P < 10⁻⁵), including after adjusting for individuals’ total number of comorbidities (eMethods and eTables 10-13 in Supplement 1).

We observed similar patterns of results for bipolar disorder PRSs and inpatient treatment (OR per SD-unit increase, 1.14 [95% CI, 1.11-1.19]; P < 10⁻¹⁷) and number of hospitalizations (β = 0.18 [95% CI, 0.11-0.26]; P < 10⁻⁵) (eTables 14-15 in Supplement 1) and between major depression PRS and inpatient treatment (OR per SD-unit increase, 1.04 [95% CI, 1.03-1.06]; P < 10⁻⁸) and number of hospitalizations (β = 0.04 [95% CI, 0.01-0.07]; P = .00764) (eTables 16-17 in Supplement 1).

Associations between schizophrenia and bipolar disorder PRSs with inpatient treatment and number of hospitalizations were replicable in participants of African ancestry (eTable 14-15 in Supplement 1).

Pleiotropic Influences of Neuropsychiatric PRS

Higher polygenic loading for schizophrenia, bipolar disorder, and major depression was associated with increased odds for numerous psychiatric diagnoses and physical health conditions (eTables 18-20 and eFigures 1-3 in Supplement 1). Associations between schizophrenia and respiratory symptoms and infections and between major depression and cardiovascular disease, hypertension, diabetes, somatic symptoms, and respiratory problems were robust among veterans without a lifetime diagnosis of psychotic, mood, or substance use disorders or who received relevant pharmacological treatment. Comparing PheWAS results, we observed relative enrichments of schizophrenia polygenic risk in mental health problems and of major depression PRSs in circulatory, respiratory, and endocrine problems, among others (eFigures 4-7 in Supplement 1).

Associations between schizophrenia PRSs and dental problems, respiratory symptoms, skin infections, substance use disorders, and suicide were replicable among veterans of African ancestry (eTables 21-23 and eFigures 8-10 in Supplement 1) and remained significant after adjusting for lifetime diagnoses and medications.

We investigated the apparent protective effects of schizophrenia PRSs¹⁰ by examining associations between individual genome-wide significant schizophrenia loci¹ and selected phecodes (Figure 3), based on the same linkage disequilibrium–independent SNVs and analytic framework as used for PheWAS. Using a simple binomial test, we found that significantly more schizophrenia-associated SNVs than expected by chance (ie, 50%) had reversed directions of associations with diabetes (168 of 270 [62.2%]; 95% CI, 0.56-0.68]; P = 7.07 × 10⁻⁵), hearing loss (165 of 270 [61.1%]; 95% CI, 0.55-0.67; P = .000313), and osteoarthritis (157 of 270 [58.1%]; 95% CI, 0.52-0.64; P = .00875). By comparison, we observed more SNVs than expected with convergent associations with bipolar disorder (191 of 270 [70.7%]; 95% CI, 0.65-0.76; P < 10⁻¹¹).

Figure 3. — A, For significant results in phenome-wide association studies of schizophrenia and major depression polygenic risk scores, the distribution of effect sizes within each disease category is displayed as a boxplot. A null effect (ie, odds ratio, 1) is denoted as a dotted line. B, Effect sizes of Psychiatric Genomics Consortium³ schizophrenia loci on bipolar disorder (phecode 296.1), sensorineural hearing loss (phecode 389.1), osteoarthritis (phecode 740.1), and diabetes (phecode 250) in Million Veteran Program participants of European ancestry. A null effect is denoted as a dotted line. Labeled loci achieved genome-wide significance in tests of the target phenotype. NEC indicates not elsewhere classified.

Genomic Structural Equation Modeling and Latent Factor PRS

Comparing our primary results with those based on latent genomic factors, we found that both schizophrenia-specific and common factor PRSs were associated with increased odds of psychosis-spectrum diagnoses (eTables 24-30 in Supplement 1). Observed protective associations of schizophrenia PRSs for sleep apnea, osteoarthritis, and hearing loss appear to be driven by schizophrenia-specific influences (eTables 24-25 in Supplement 1). The majority of associations between PRSs and broader psychiatric diagnoses and physical health problems were driven by a shared genetic liability (eTables 29-30 in Supplement 1).

Polygenic Validation of the Psychosis-Affective Spectrum

We further explored the transdiagnostic spectrum concept via hierarchical assignments of participants to schizophrenia, bipolar disorder, major depression, or related diagnoses; schizoaffective disorders, bipolar II disorder, cyclothymia, and dysthymia were considered as intermediate categories of illness and were included in analyses given adequate sample sizes (eTables 31-32 in Supplement 1). Figure 4 displays estimated PRSs estimates across disorders, comparing individuals with these disorders to a common set of screened controls.

Figure 4. — A, Loadings of a common factor on schizophrenia (SCZ), bipolar disorder (BIP), and major depression (MDD) results and residual variances corresponding to disorder-specific effects. B, Odds ratios per SD unit increase in PRS for selected diagnoses compared against a common set of controls; analogous results based on latent, disorder-specific PRS appear are plotted in lighter hues. Case assignments were hierarchical and nonoverlapping. DEP indicates depression; PGC, Psychiatric Genomics Consortium.

Discussion

Building on our previous reports that published GWAS results are robustly generalizable to the US veteran population,²⁵ we have demonstrated that the penetrance of schizophrenia PRSs is equivalent across VA and civilian health care systems, despite marked differences in absolute prevalence.¹⁰ Leveraging the VA’s extensive EHR, we confirm and extend reported associations between neuropsychiatric PRSs and broad disease categories in approximately 400 000 individuals. We derived novel, latent factors indexing disorder-specific and shared cross-disorder risk and attempted to disentangle widespread pleiotropy from confounding through extensive secondary modeling.

We first validated an EHR-based phenotyping approach in an embedded, well-characterized cohort with confirmed diagnoses of schizophrenia or bipolar 1 disorder and found that a simple approach¹⁰ requiring 2 or more ICD-9/10 codes correctly identified approximately 95% of cases. Applying this phenotyping strategy to the full MVP cohort, we detected prevalence of schizophrenia, bipolar disorder, and major depression that are several-fold higher than in the general population, although representative of the US veteran population at large.²³ Rates of schizophrenia and major depression among veterans of African ancestry were markedly elevated compared with veterans of European ancestry, which may be suggestive of implicit bias in diagnosis,²⁶ self-selection bias for VHA utilization, limited alternatives for health care, or other structural issues.

Critically, despite markedly higher prevalence of schizophrenia, bipolar disorder, and major depression in the VA health care system, we did not find predictive values to be meaningfully attenuated. Schizophrenia PRSs yielded effect sizes for participants of European ancestry that were within the 95% CI of those reported by the PsychEMERGE consortium.¹⁰ The latter suggests that the penetrance of within-population relative PRSs in the US veteran population is equivalent to that of civilian cohorts despite nonrandom recruitment and exposure to distinct environmental factors and experiences.

Current neuropsychiatric PRSs were robustly associated with a range of psychiatric problems. Higher schizophrenia PRSs also increased risk of suicide, obsessive compulsive disorder and personality disorders, anxiety, and substance use behaviors, as well as a host of physical and somatic symptoms, recapitulating recent findings based on 4 US civilian health care systems.¹⁰ Notably, increased risks of certain infections and dental problems were detectable even when adjusting for psychotic and affective diagnoses and treatment, and excluding diagnosed substance use disorders, suggesting that neuropsychiatric liability may be penetrant even in individuals who lack a formal diagnosis. Other associations, such as those observed with erectile dysfunction and polydipsia, were explained by relevant diagnoses and adverse reactions of prescribed medications.²⁷

We observed protective associations of schizophrenia PRSs with hearing loss, osteoarthritis, and diabetes, in contrast to widely documented, risk-increasing iatrogenic effects of second-generation antipsychotics.²⁸ Post hoc analyses of schizophrenia-associated loci¹ revealed an enrichment of SNVs at which the schizophrenia risk allele is protective for these conditions, suggestive of antagonistic pleiotropic effects.

Schizophrenia-specific PRSs were primarily associated with diagnoses of schizophrenia, paranoid disorders, psychosis, and schizoid personality disorder but only modestly with bipolar disorder, evincing some fidelity of published GWAS to Kraepelinian dichotomy. Intriguingly, major depression-specific and common factor PRSs, but not schizophrenia-specific PRSs, were strongly associated with increased number of comorbidities, suggesting that underlying confounding may be driving many of the observed pleiotropic associations. Future studies using mendelian randomization and within-family approaches may help to determine whether these findings are due to some likely causal influence or accounted for by other shared environmental risk factors (eg, socioeconomic status).

Strikingly, the higher prevalence of schizophrenia and major depression among veterans of African ancestry were largely not associated with individuals’ risk strata; for example, only veterans of European ancestry in the uppermost schizophrenia PRS decile had absolute risk comparable with those of African ancestry in the lowest decile. Despite lower cross-population generalizability of bipolar disorder and major depression PRSs, comparisons of individuals of African ancestry at extremes of PRSs yielded significant associations.

Limitations

We did not attempt to model environmental or experiential differences associated with participants’ military service, which may partially explain increased rates of some illnesses. We did not specifically investigate the implications of predominantly male ascertainment in MVP. CSP #572 participants largely served in the period between the Vietnam War and Gulf War conflicts, while MVP participants’ service eras were more broadly distributed.

Because available EHR data are restricted to treatment received at VA facilities, any relevant medical history outside the VHA health system, including before or during participants’ military service, is limited to self-report.

We focused on populations of African and European ancestry in the current study because these broadly defined ancestries comprised the majority of the CSP #572 and MVP cohorts. Our ongoing work in this area will extend these findings to diverse Asian, Hispanic, and Latino populations.

Conclusions

Application of current neuropsychiatric PRSs to the MVP yielded results consistent with multiple, continuous liability distributions underlying schizophrenia, bipolar disorder, and major depression,^29,30 underscoring the advantages of multivariate and transdiagnostic approaches for studying these complex, heterogeneous clinical presentations.

Supplement 1.

eMethods.

eTable 1. ICD-9/10 billing codes queried from VHA Corporate Data Warehouse

eTable 2. Medications queried from VHA prescription records

eTable 3. CSP #572 and MVP participants meeting varying EHR-based criteria (females only)

eTable 4. Factors associated with diagnostic misclassification of BPI cases (as SCZ) in CSP #572

eTable 5. Factors associated with diagnostic misclassification of SCZ cases (as BPI) in CSP #572

eTable 6. Predictive models for SCZ or BPI diagnosis based on EHR

eTable 7. Associations of neuropsychiatric PRSs with SCZ

eTable 8. Associations of neuropsychiatric PRSs with BIP

eTable 9. Associations of neuropsychiatric PRSs with DEP

eTable 10. Associations of neuropsychiatric PRSs with total comorbidity burden among EA participants

eTable 11. Associations of neuropsychiatric PRSs with total comorbidity burden among AA participants

eTable 12. Associations of neuropsychiatric PRSs with inpatient treatment for SCZ

eTable 13. Associations of neuropsychiatric PRSs with number of hospitalizations for SCZ

eTable 14. Associations of neuropsychiatric PRSs with inpatient treatment for BIP

eTable 15. Associations of neuropsychiatric PRSs with number of hospitalizations for BIP

eTable 16. Associations of neuropsychiatric PRSs with inpatient treatment for DEP

eTable 17. Associations of neuropsychiatric PRSs with number of hospitalizations for DEP

eTable 18. Significant findings (p<10^-25) in PheWAS of SCZ PRS in EA participants

eTable 19. Significant findings (p<10^-25) in PheWAS of BIP PRS in EA participants

eTable 20. Significant findings (p<10^-25) in PheWAS of DEP PRS in EA participants

eTable 21. Significant findings (p<10^-5) in PheWAS of SCZ PRS in AA participants

eTable 22. Significant findings (p<10^-5) in PheWAS of BIP PRS in AA participants

eTable 23. Significant findings (p<10^-5) in PheWAS of DEP PRS in AA participants

eTable 24. Significant findings (p<10^-25) in PheWAS of SCZ-specific PRS in EA participants

eTable 25. Significant findings (p<10^-5) in PheWAS of SCZ-specific PRS in AA participants

eTable 26. Significant findings (p<10^-25) in PheWAS of BIP-specific PRS in EA participants

eTable 27. Significant findings (p<10^-25) in PheWAS of DEP-specific PRS in EA participants

eTable 28. Significant findings (p<10^-5) in PheWAS of DEP-specific PRS in AA participants

eTable 29. Significant findings (p<10^-25) in PheWAS of common factor PRS in EA participants

eTable 30. Significant findings (p<10^-5) in PheWAS of common factor PRS in AA participants

eTable 31. Associations of neuropsychiatric PRSs with hierarchical diagnoses in EA participants

eTable 32. Associations of neuropsychiatric PRSs with hierarchical diagnoses in AA participants

eFigure 1. Odds ratios for top decile of SCZ PRS in EA participants

eFigure 2. Odds ratios for top deciles of BIP PRS in EA participants

eFigure 3. Odds ratios for top decile of DEP PRS in EA participants

eFigure 4. PheWAS results for neuropsychiatric PRS

eFigure 5. Relative enrichments of SCZ versus BIP PheWAS results across disease categories

eFigure 6. Relative enrichments of SCZ and DEP PheWAS results across disease categories

eFigure 7. Relative enrichments of BIP and DEP PheWAS results across disease categories

eFigure 8. Odds ratios for top deciles of SCZ PRS in AA participants

eFigure 9. Odds ratios for top deciles of BIP PRS in AA participants

eFigure 10. Odds ratios for top deciles of DEP PRS in AA participants

Click here for additional data file.^{(5.1MB, pdf)}

Supplement 2.

Nonauthor collaborators. The Cooperative Studies Program (CSP) #572 and Million Veteran Program (MVP) investigators

Click here for additional data file.^{(568.7KB, pdf)}

References

1.Trubetskoy V, Pardiñas AF, Qi T, et al. ; Indonesia Schizophrenia Consortium; PsychENCODE; Psychosis Endophenotypes International Consortium; SynGO Consortium; Schizophrenia Working Group of the Psychiatric Genomics Consortium . Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022;604(7906):502-508. doi: 10.1038/s41586-022-04434-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Mullins N, Forstner AJ, O’Connell KS, et al. Genome-wide association study of over 40,000 bipolar disorder cases provides novel biological insights. medRxiv. Preprint posted September 18, 2020. doi: 10.1101/2020.09.17.20187054https://www.medrxiv.org/content/10.1101/2020.09.17.20187054v1.full.pdf+html [DOI]
3.Wray NR, Ripke S, Mattheisen M, et al. ; eQTLGen; 23andMe; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium . Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50(5):668-681. doi: 10.1038/s41588-018-0090-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Lichtenstein P, Yip BH, Björk C, et al. Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet. 2009;373(9659):234-239. doi: 10.1016/S0140-6736(09)60072-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Rees E, O’Donovan MC, Owen MJ. Genetics of schizophrenia. Curr Opin Behav Sci. 2015;2:8-14. doi: 10.1016/j.cobeha.2014.07.001 [DOI] [Google Scholar]
6.Purcell SM, Wray NR, Stone JL, et al. ; International Schizophrenia Consortium . Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460(7256):748-752. doi: 10.1038/nature08185 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Khera AV, Chaffin M, Aragam KG, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50(9):1219-1224. doi: 10.1038/s41588-018-0183-z [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Murray GK, Lin T, Austin J, McGrath JJ, Hickie IB, Wray NR. Could polygenic risk scores be useful in psychiatry?: a review. JAMA Psychiatry. 2021;78(2):210-219. doi: 10.1001/jamapsychiatry.2020.3042 [DOI] [PubMed] [Google Scholar]
9.Denny JC, Ritchie MD, Basford MA, et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics. 2010;26(9):1205-1210. doi: 10.1093/bioinformatics/btq126 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Zheutlin AB, Dennis J, Karlsson Linnér R, 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(10):846-855. doi: 10.1176/appi.ajp.2019.18091085 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Gaziano JM, Concato J, Brophy M, et al. Million Veteran Program: A mega-biobank to study genetic influences on health and disease. J Clin Epidemiol. 2016;70:214-223. doi: 10.1016/j.jclinepi.2015.09.016 [DOI] [PubMed] [Google Scholar]
12.Boersma P. Multiple chronic conditions among veterans and nonveterans: United States, 2015–2018. Nat Health Stat Reports. 2021;153:1-13. doi: 10.15620/cdc:101659 [DOI] [PubMed]
13.Kramarow EA, Pastor PN. The health of male veterans and nonveterans aged 25-64: United States, 2007-2010. NCHS Data Brief. 2012;(101):1-8. [PubMed] [Google Scholar]
14.Harvey PD, Siever LJ, Huang GD, et al. The genetics of functional disability in schizophrenia and bipolar illness: Methods and initial results for VA cooperative study #572. Am J Med Genet B Neuropsychiatr Genet. 2014;165B(4):381-389. doi: 10.1002/ajmg.b.32242 [DOI] [PubMed] [Google Scholar]
15.First MB, Spitzer RL, Gibbon M, Williams JBW. User’s Guide for the Structured Clinical Interview for DSM-IV Axis I Disorders SCID-I: Clinician Version. American Psychiatric Pub; 1997. [Google Scholar]
16.Mausbach BT, Harvey PD, Goldman SR, Jeste DV, Patterson TL. Development of a brief scale of everyday functioning in persons with serious mental illness. Schizophr Bull. 2007;33(6):1364-1372. doi: 10.1093/schbul/sbm014 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Hunter-Zinck H, Shi Y, Li M, et al. ; VA Million Veteran Program . Genotyping array design and data quality control in the Million Veteran Program. Am J Hum Genet. 2020;106(4):535-548. doi: 10.1016/j.ajhg.2020.03.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Fang H, Hui Q, Lynch J, et al. ; VA Million Veteran Program . Harmonizing genetic ancestry and self-identified race/ethnicity in genome-wide association studies. Am J Hum Genet. 2019;105(4):763-772. doi: 10.1016/j.ajhg.2019.08.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Ge T, Chen CY, Ni Y, Feng YA, Smoller JW. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun. 2019;10(1):1776. doi: 10.1038/s41467-019-09718-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Grotzinger AD, Rhemtulla M, de Vlaming R, et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat Hum Behav. 2019;3(5):513-525. doi: 10.1038/s41562-019-0566-x [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Kessler RC, Birnbaum H, Demler O, et al. The prevalence and correlates of nonaffective psychosis in the National Comorbidity Survey Replication (NCS-R). Biol Psychiatry. 2005;58(8):668-676. doi: 10.1016/j.biopsych.2005.04.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Kessler RC, Chiu WT, Demler O, Merikangas KR, Walters EE. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):617-627. doi: 10.1001/archpsyc.62.6.617 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Trivedi RB, Post EP, Sun H, et al. Prevalence, comorbidity, and prognosis of mental health among US veterans. Am J Public Health. 2015;105(12):2564-2569. doi: 10.2105/AJPH.2015.302836 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Meier SM, Agerbo E, Maier R, et al. ; MooDS SCZ Consortium . High loading of polygenic risk in cases with chronic schizophrenia. Mol Psychiatry. 2016;21(7):969-974. doi: 10.1038/mp.2015.130 [DOI] [PubMed] [Google Scholar]
25.Bigdeli TB, Fanous AH, Li Y, et al. ; Consortium on the Genetics of Schizophrenia (COGS); Genomic Psychiatry Cohort (GPC) Investigators; Million Veteran Program (MVP) . Genome-wide association studies of schizophrenia and bipolar disorder in a diverse cohort of US veterans. Schizophr Bull. 2021;47(2):517-529. doi: 10.1093/schbul/sbaa133 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Dueñas HR, Seah C, Johnson JS, Huckins LM. Implicit bias of encoded variables: frameworks for addressing structured bias in EHR-GWAS data. Hum Mol Genet. 2020;29(R1):R33-R41. doi: 10.1093/hmg/ddaa192 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Wang MT, Tsai CL, Lin CW, Yeh CB, Wang YH, Lin HL. Association between antipsychotic agents and risk of acute respiratory failure in patients with chronic obstructive pulmonary disease. JAMA Psychiatry. 2017;74(3):252-260. doi: 10.1001/jamapsychiatry.2016.3793 [DOI] [PubMed] [Google Scholar]
28.Annamalai A, Kosir U, Tek C. Prevalence of obesity and diabetes in patients with schizophrenia. World J Diabetes. 2017;8(8):390-396. doi: 10.4239/wjd.v8.i8.390 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Kendler KS, Neale MC, Walsh D. Evaluating the spectrum concept of schizophrenia in the Roscommon Family Study. Am J Psychiatry. 1995;152(5):749-754. doi: 10.1176/ajp.152.5.749 [DOI] [PubMed] [Google Scholar]
30.Bigdeli TB, Bacanu SA, Webb BT, et al. Molecular validation of the schizophrenia spectrum. Schizophr Bull. 2014;40(1):60-65. doi: 10.1093/schbul/sbt122 [DOI] [PMC free article] [PubMed] [Google Scholar]

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Supplementary Materials