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Principal Component Analysis Reduces Collider Bias in Polygenic Score Effect Size Estimation

Abstract

In this study, we test principal component analysis (PCA) of measured confounders as a method to reduce collider bias in polygenic association models. We present results from simulations and application of the method in the Collaborative Study of the Genetics of Alcoholism (COGA) sample with a polygenic score for alcohol problems, DSM-5 alcohol use disorder as the target phenotype, and two collider variables: tobacco use and educational attainment. Simulation results suggest that assumptions regarding the correlation structure and availability of measured confounders are complementary, such that meeting one assumption relaxes the other. Application of the method in COGA shows that PC covariates reduce collider bias when tobacco use is used as the collider variable. Application of this method may improve PRS effect size estimation in some cases by reducing the effect of collider bias, making efficient use of data resources that are available in many studies.

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Availability of Data and Material

Data from the Collaborative Study on the Genetics of Alcoholism (COGA) are available via dbGaP (phs000763.v1.p1, phs000125.v1.p1) or through the National Institute on Alcohol Abuse and Alcoholism.

Code Availability

The R scripts used in this work are available on GitHub at https://github.com/thomasns0/PCA_Collider.git.

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Acknowledgements

The Collaborative Study on the Genetics of Alcoholism (COGA), Principal Investigators B. Porjesz, V. Hesselbrock, T. Foroud; Scientific Director, A. Agrawal; Translational Director, D. Dick, includes eleven different centers: University of Connecticut (V. Hesselbrock); Indiana University (H.J. Edenberg, T. Foroud, Y. Liu, M. Plawecki); University of Iowa Carver College of Medicine (S. Kuperman, J. Kramer); SUNY Downstate Health Sciences University (B. Porjesz, J. Meyers, C. Kamarajan, A. Pandey); Washington University in St. Louis (L. Bierut, J. Rice, K. Bucholz, A. Agrawal); University of California at San Diego (M. Schuckit); Rutgers University (J. Tischfield, R. Hart, J. Salvatore); The Children’s Hospital of Philadelphia, University of Pennsylvania (L. Almasy); Virginia Commonwealth University (D. Dick); Icahn School of Medicine at Mount Sinai (A. Goate, P. Slesinger); and Howard University (D. Scott). Other COGA collaborators include: L. Bauer (University of Connecticut); J. Nurnberger Jr., L. Wetherill, X., Xuei, D. Lai, S. O’Connor, (Indiana University); G. Chan (University of Iowa; University of Connecticut); D.B. Chorlian, J. Zhang, P. Barr, S. Kinreich, G. Pandey (SUNY Downstate); N. Mullins (Icahn School of Medicine at Mount Sinai); A. Anokhin, S. Hartz, E. Johnson, V. McCutcheon, S. Saccone (Washington University); J. Moore, Z. Pang, S. Kuo (Rutgers University); A. Merikangas (The Children’s Hospital of Philadelphia and University of Pennsylvania); F. Aliev (Virginia Commonwealth University); H. Chin and A. Parsian are the NIAAA Staff Collaborators. We continue to be inspired by our memories of Henri Begleiter and Theodore Reich, founding PI and Co-PI of COGA, and also owe a debt of gratitude to other past organizers of COGA, including Ting- Kai Li, P. Michael Conneally, Raymond Crowe, and Wendy Reich, for their critical contributions. This national collaborative study is supported by NIH Grant U10AA008401 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA). This work was also supported by the National Institutes of Health (NIH) Grants R01AA028064 (PI: Salvatore) and K01AA024152 (PI: Salvatore) from the National Institute on Alcohol Abuse and Alcoholism (NIAAA).

Funding

This work was supported by the National Institutes of Health (NIH) Grants R01AA028064 (PI: Salvatore) and K01AA024152 (PI: Salvatore) from the National Institute on Alcohol Abuse and Alcoholism (NIAAA). The Collaborative Study on the Genetics of Alcoholism (COGA) is supported by NIH Grant U10AA008401 (PI: Porjesz).

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Authors and Affiliations

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Contributions

Nathaniel S. Thomas: conceived of the study, conducted statistical analyses, and wrote the manuscript. Peter Barr, Fazil Aliev, Mallory Stephenson, and Sally I-Chun Kuo assisted with the design and implementation of the study and provided editorial feedback on the whole manuscript. Grace Chan, Danielle M. Dick, Howard J. Edenberg, Victor Hesselbrock, and Chella Kamarajan provided editorial feedback on the whole manuscript. Jessica E. Salvatore supervised the design and implementation of the study and provided editorial feedback on the whole manuscript. All authors contributed to and have approved the final manuscript.

Corresponding author

Correspondence to Nathaniel S. Thomas.

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Conflicts of Interest/Competing Interests

Nathaniel S. Thomas, Peter Barr, Fazil Aliev, Mallory Stephenson, Sally I-Chun Kuo, Grace Chan, Danielle M. Dick, Howard J. Edenberg, Victor Hesselbrock, Chella Kamarajan, and Jessica E. Salvatore declare that they have no conflicts of interest.

Ethics Approval

The Institutional Review Board at all data collection sites approved the study.

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Written consent was obtained from all participants.

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Thomas, N.S., Barr, P., Aliev, F. et al. Principal Component Analysis Reduces Collider Bias in Polygenic Score Effect Size Estimation. Behav Genet 52, 268–280 (2022). https://doi.org/10.1007/s10519-022-10104-z

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