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Impact of selection bias on polygenic risk score estimates in healthcare settings

Published online by Cambridge University Press:  25 May 2023

Younga Heather Lee Open the ORCID record for Younga Heather Lee [Opens in a new window] ,
Tanayott Thaweethai ,
Yi-Han Sheu ,
Yen-Chen Anne Feng ,
Elizabeth W. Karlson ,
Tian Ge ,
Peter Kraft  and
Jordan W. Smoller Open the ORCID record for Jordan W. Smoller [Opens in a new window]
Younga Heather Lee
Affiliation:
Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA Harvard Medical School, Boston, Massachusetts, USA
Tanayott Thaweethai
Affiliation:
Harvard Medical School, Boston, Massachusetts, USA Biostatistics Center, Massachusetts General Hospital, Boston, Massachusetts, USA
Yi-Han Sheu
Affiliation:
Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA Harvard Medical School, Boston, Massachusetts, USA
Yen-Chen Anne Feng
Affiliation:
Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA Harvard Medical School, Boston, Massachusetts, USA Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA Division of Biostatistics and Data Science, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
Elizabeth W. Karlson
Affiliation:
Harvard Medical School, Boston, Massachusetts, USA Division of Rheumatology, Immunity, and Inflammation, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
Tian Ge
Affiliation:
Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA Harvard Medical School, Boston, Massachusetts, USA Center for Precision Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
Peter Kraft
Affiliation:
Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
Jordan W. Smoller*
Affiliation:
Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA Harvard Medical School, Boston, Massachusetts, USA Center for Precision Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA
*
Corresponding author: Jordan W. Smoller; Email: jsmoller@mgh.harvard.edu
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Abstract

Background

Hospital-based biobanks are being increasingly considered as a resource for translating polygenic risk scores (PRS) into clinical practice. However, since these biobanks originate from patient populations, there is a possibility of bias in polygenic risk estimation due to overrepresentation of patients with higher frequency of healthcare interactions.

Methods

PRS for schizophrenia, bipolar disorder, and depression were calculated using summary statistics from the largest available genomic studies for a sample of 24 153 European ancestry participants in the Mass General Brigham (MGB) Biobank. To correct for selection bias, we fitted logistic regression models with inverse probability (IP) weights, which were estimated using 1839 sociodemographic, clinical, and healthcare utilization features extracted from electronic health records of 1 546 440 non-Hispanic White patients eligible to participate in the Biobank study at their first visit to the MGB-affiliated hospitals.

Results

Case prevalence of bipolar disorder among participants in the top decile of bipolar disorder PRS was 10.0% (95% CI 8.8–11.2%) in the unweighted analysis but only 6.2% (5.0–7.5%) when selection bias was accounted for using IP weights. Similarly, case prevalence of depression among those in the top decile of depression PRS was reduced from 33.5% (31.7–35.4%) to 28.9% (25.8–31.9%) after IP weighting.

Conclusions

Non-random selection of participants into volunteer biobanks may induce clinically relevant selection bias that could impact implementation of PRS in research and clinical settings. As efforts to integrate PRS in medical practice expand, recognition and mitigation of these biases should be considered and may need to be optimized in a context-specific manner.

Keywords

selection biaspolygenic risk scorebiobankinverse probability weightingcausal inference
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 15 , November 2023 , pp. 7435 - 7445
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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