Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four health care systems

Amanda B. Zheutlin; Jessica Dennis; Richard Karlsson Linnér; Arden Moscati; Nicole Restrepo; Peter Straub; Douglas Ruderfer; Victor M. Castro; Chia Yen Chen; Tian Ge; Laura M. Huckins; Alexander Charney; H. Lester Kirchner; Eli A. Stahl; Christopher F. Chabris; Lea K. Davis; Jordan W. Smoller

doi:10.1176/appi.ajp.2019.18091085

Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four health care systems

Amanda B. Zheutlin, Jessica Dennis, Richard Karlsson Linnér, Arden Moscati, Nicole Restrepo, Peter Straub, Douglas Ruderfer, Victor M. Castro, Chia Yen Chen, Tian Ge, Laura M. Huckins, Alexander Charney, H. Lester Kirchner, Eli A. Stahl, Christopher F. Chabris, Lea K. Davis, Jordan W. Smoller^*

^*Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

Objective: Individuals at high risk for schizophrenia may benefit from early intervention, but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts. The authors sought to test the utility of this approach in clinical settings and to evaluate the broader health consequences of high genetic risk for schizophrenia. Methods: The authors used electronic health records for 106,160 patients from four health care systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from summary statistics and tested for association with 1,359 disease categories, including schizophrenia and psychosis, in phenome-wide association studies. Effects were combined through meta-analysis across sites. Results: PRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS, 1.55; 95% CI=1.4, 1.7), and patients in the highest risk decile of the PRS distribution had up to 4.6-fold higher odds of schizophrenia compared with those in the bottom decile (95% CI=2.9, 7.3). PRSs were also positively associated with other phenotypes, including anxiety, mood, substance use, neurological, and personality disorders, as well as suicidal behavior, memory loss, and urinary syndromes; they were inversely related to obesity. Conclusions: The study demonstrates that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in health care settings and has pleiotropic effects on related psychiatric disorders as well as other medical syndromes. The results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in health care systems.

Original language	English
Pages (from-to)	846-855
Number of pages	10
Journal	American Journal of Psychiatry
Volume	176
Issue number	10
DOIs	https://doi.org/10.1176/appi.ajp.2019.18091085
Publication status	Published - 1 Oct 2019

Funding

Presented at the World Congress of Psychiatric Genetics, Glasgow, Scotland, October 11–15, 2018. Supported in part by NIMH grant R01MH118233 (to Drs. Smoller and Davis). Dr. Ge was supported in part by National Institute on Aging grant K99AG054573, Dr. Dennis was supported by the Canadian Institutes of Health Research (MFE-142936), Dr. Stahl was supported in part by NIMH grants U01MH109536, R01MH106531, and R01MH095034, Dr. Davis was supported by NIMH grant R01MH113362, Dr. Ruderfer was supported by NIMH grant R01MH111776, and Dr. Smoller was supported in part by National Human Genome Research Institute grant U01HG008685 supporting the eMERGE Network; he is also a Tepper Family MGH Research Scholar and is supported in part by the Demarest Lloyd, Jr. Foundation. The VUMC components of the project were conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville. The data sets used for the project were obtained from Vanderbilt University Medical Center’s Synthetic Derivative and BioVU, which are supported by numerous sources: institutional funding, private agencies, and federal grants. These include the NIH-funded Shared Instrumentation Grant S10RR025141 and CTSA grants UL1TR002243, UL1TR000445, and UL1RR024975 from the National Center for Advancing Translational Sciences. Additional funding was provided by NIH through grants P50GM115305 and U19HL065962. The Mount Sinai BioMe Biobank is supported by the Andrea and Charles Bronfman Philanthropies. The authors acknowledge the expert technical support of the VANTAGE and VANGARD core facilities, supported in part by the Vanderbilt-Ingram Cancer Center (P30 CA068485) and Vanderbilt Vision Center (P30 EY08126). The authors also acknowledge the Partners Biobank for providing samples, genomic data, and health information data. The authors thank the MyCode Community Health Initiative participants for their permission to utilize their health and genomics information in the Dis-covEHR collaboration. The DiscovEHR study was funded in part by the Regeneron Genetics Center, and the Geisinger Clinic was funded by grant U01HG008679.

Funders	Funder number
BioVU	S10RR025141, UL1RR024975
Regeneron Genetics Center
Vanderbilt Vision Center	P30 EY08126
National Institutes of Health	U19HL065962, P50GM115305
National Institute of Mental Health	R01MH118233
National Institute on Aging	K99AG054573
National Human Genome Research Institute	U01HG008679, U01HG008685
National Center for Advancing Translational Sciences	UL1TR000445, UL1TR002243
Vanderbilt-Ingram Cancer Center	P30 CA068485
Vanderbilt University Medical Center
Canadian Institutes of Health Research	R01MH111776, MFE-142936, R01MH113362, U01MH109536, R01MH095034, R01MH106531

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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Zheutlin, A. B., Dennis, J., Linnér, R. K., Moscati, A., Restrepo, N., Straub, P., Ruderfer, D., Castro, V. M., Chen, C. Y., Ge, T., Huckins, L. M., Charney, A., Kirchner, H. L., Stahl, E. A., Chabris, C. F., Davis, L. K., & Smoller, J. W. (2019). Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four health care systems. American Journal of Psychiatry, 176(10), 846-855. https://doi.org/10.1176/appi.ajp.2019.18091085

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title = "Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four health care systems",

abstract = "Objective: Individuals at high risk for schizophrenia may benefit from early intervention, but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts. The authors sought to test the utility of this approach in clinical settings and to evaluate the broader health consequences of high genetic risk for schizophrenia. Methods: The authors used electronic health records for 106,160 patients from four health care systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from summary statistics and tested for association with 1,359 disease categories, including schizophrenia and psychosis, in phenome-wide association studies. Effects were combined through meta-analysis across sites. Results: PRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS, 1.55; 95% CI=1.4, 1.7), and patients in the highest risk decile of the PRS distribution had up to 4.6-fold higher odds of schizophrenia compared with those in the bottom decile (95% CI=2.9, 7.3). PRSs were also positively associated with other phenotypes, including anxiety, mood, substance use, neurological, and personality disorders, as well as suicidal behavior, memory loss, and urinary syndromes; they were inversely related to obesity. Conclusions: The study demonstrates that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in health care settings and has pleiotropic effects on related psychiatric disorders as well as other medical syndromes. The results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in health care systems.",

author = "Zheutlin, {Amanda B.} and Jessica Dennis and Linn{\'e}r, {Richard Karlsson} and Arden Moscati and Nicole Restrepo and Peter Straub and Douglas Ruderfer and Castro, {Victor M.} and Chen, {Chia Yen} and Tian Ge and Huckins, {Laura M.} and Alexander Charney and Kirchner, {H. Lester} and Stahl, {Eli A.} and Chabris, {Christopher F.} and Davis, {Lea K.} and Smoller, {Jordan W.}",

year = "2019",

month = oct,

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doi = "10.1176/appi.ajp.2019.18091085",

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publisher = "American Psychiatric Association",

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Zheutlin, AB, Dennis, J, Linnér, RK, Moscati, A, Restrepo, N, Straub, P, Ruderfer, D, Castro, VM, Chen, CY, Ge, T, Huckins, LM, Charney, A, Kirchner, HL, Stahl, EA, Chabris, CF, Davis, LK & Smoller, JW 2019, 'Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four health care systems', American Journal of Psychiatry, vol. 176, no. 10, pp. 846-855. https://doi.org/10.1176/appi.ajp.2019.18091085

TY - JOUR

T1 - Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four health care systems

AU - Zheutlin, Amanda B.

AU - Dennis, Jessica

AU - Linnér, Richard Karlsson

AU - Moscati, Arden

AU - Restrepo, Nicole

AU - Straub, Peter

AU - Ruderfer, Douglas

AU - Castro, Victor M.

AU - Chen, Chia Yen

AU - Ge, Tian

AU - Huckins, Laura M.

AU - Charney, Alexander

AU - Kirchner, H. Lester

AU - Stahl, Eli A.

AU - Chabris, Christopher F.

AU - Davis, Lea K.

AU - Smoller, Jordan W.

PY - 2019/10/1

Y1 - 2019/10/1

N2 - Objective: Individuals at high risk for schizophrenia may benefit from early intervention, but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts. The authors sought to test the utility of this approach in clinical settings and to evaluate the broader health consequences of high genetic risk for schizophrenia. Methods: The authors used electronic health records for 106,160 patients from four health care systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from summary statistics and tested for association with 1,359 disease categories, including schizophrenia and psychosis, in phenome-wide association studies. Effects were combined through meta-analysis across sites. Results: PRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS, 1.55; 95% CI=1.4, 1.7), and patients in the highest risk decile of the PRS distribution had up to 4.6-fold higher odds of schizophrenia compared with those in the bottom decile (95% CI=2.9, 7.3). PRSs were also positively associated with other phenotypes, including anxiety, mood, substance use, neurological, and personality disorders, as well as suicidal behavior, memory loss, and urinary syndromes; they were inversely related to obesity. Conclusions: The study demonstrates that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in health care settings and has pleiotropic effects on related psychiatric disorders as well as other medical syndromes. The results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in health care systems.

AB - Objective: Individuals at high risk for schizophrenia may benefit from early intervention, but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts. The authors sought to test the utility of this approach in clinical settings and to evaluate the broader health consequences of high genetic risk for schizophrenia. Methods: The authors used electronic health records for 106,160 patients from four health care systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from summary statistics and tested for association with 1,359 disease categories, including schizophrenia and psychosis, in phenome-wide association studies. Effects were combined through meta-analysis across sites. Results: PRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS, 1.55; 95% CI=1.4, 1.7), and patients in the highest risk decile of the PRS distribution had up to 4.6-fold higher odds of schizophrenia compared with those in the bottom decile (95% CI=2.9, 7.3). PRSs were also positively associated with other phenotypes, including anxiety, mood, substance use, neurological, and personality disorders, as well as suicidal behavior, memory loss, and urinary syndromes; they were inversely related to obesity. Conclusions: The study demonstrates that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in health care settings and has pleiotropic effects on related psychiatric disorders as well as other medical syndromes. The results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in health care systems.

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Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four health care systems

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