Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia in 106,160 Patients Across Four Health Care Systems

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

doi:10.1176/appi.ajp.2019.18091085

Cited by 184 publications

(201 citation statements)

References 49 publications

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“…The PRS-PCA approach controls Type I error while maintaining good power. This approach is well-suited to hypothesis testing with many PRSs, because it prevents overfitting each PRS to the outcome and does not require choosing one p-value threshold for all PRSs (Mullins et al, 2019;Richardson et al, 2019;Zheutlin et al, 2019), which can reduce power as seen in our simulations. In this study, we explored how the PRS-PCA approach can improve PRS analyses that implement P+T, by using the F I G U R E 2 Scaled weights on effect sizes of SNPs (betas) for SCZ-PRS assigned by PRS-PCA in the Mayo Clinic sample.…”

Section: Discussionmentioning

confidence: 99%

A principal component approach to improve association testing with polygenic risk scores

Coombes

Ploner

Bergen

et al. 2020

Genetic Epidemiology

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Polygenic risk scores (PRSs) have become an increasingly popular approach for demonstrating polygenic influences on complex traits and for establishing common polygenic signals between different traits. PRSs are typically constructed using pruning and thresholding (P+T), but the best choice of parameters is uncertain; thus multiple settings are used and the best is chosen. Optimization can lead to inflated Type I error. Permutation procedures can correct this, but they can be computationally intensive. Alternatively, a single parameter setting can be chosen a priori for the PRS, but choosing suboptimal settings results in loss of power. We propose computing PRSs under a range of parameter settings, performing principal component analysis (PCA) on the resulting set of PRSs, and using the first PRS–PC in association tests. The first PC reweights the variants included in the PRS to achieve maximum variation over all PRS settings used. Using simulations and a real data application to study PRS association with bipolar disorder and psychosis in bipolar disorder, we compare the performance of the proposed PRS–PCA approach with a permutation test and an a priori selected p‐value threshold. The PRS–PCA approach is simple to implement, outperforms the other strategies in most scenarios, and provides an unbiased estimate of prediction performance.

show abstract

Section: Discussionmentioning

confidence: 99%

A principal component approach to improve association testing with polygenic risk scores

Coombes

Ploner

Bergen

et al. 2020

Genetic Epidemiology

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show abstract

“…The PRS-PCA approach was designed to control type I error while maintaining good power. This approach is most suited to hypothesis testing with many PRSs because it prevents overfitting each PRS to the outcome and does not require choosing one p-value threshold for all PRSs 8,9,25 , which can reduce power. In this paper, we explored how the PRS-PCA approach can improve PRS analyses that implement P+T.…”

Section: Discussionmentioning

confidence: 99%

“…In this paper, we propose a new powerful method of testing for association of PRSs with a phenotype, which avoids the multiple testing inherent in the popular optimization approach. In studies that aim to test for association of PRSs with more than one phenotype such as a PRS PheWAS 8 , the PRS-PCA approach would substantially reduce the multiple testing that would occur with the optimization approach. With the growing use of PRSs, the PRS-PCA approach gives researchers an unbiased and powerful approach to dissect polygenic risk of phenotypes.…”

Section: Discussionmentioning

confidence: 99%

“…fixing the p-value threshold and LD pruning level) to construct a single PRS. This approach was used recently in two different investigations to test for association of one PRS with many phenotypes 8,9 . By not optimizing over a set of tuning parameters for each test of association, this strategy avoids further increasing the multiple testing and computation time.…”

Section: Introductionmentioning

confidence: 99%

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A principal component approach to improve association testing with polygenic risk scores

Coombes

Biernacka

2019

Preprint

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Polygenic risk scores (PRSs) have become an increasingly popular approach for demonstrating polygenic influences on complex traits and for establishing common polygenic signals between different traits. PRSs are typically constructed using pruning and thresholding (P+T), but the best choice of parameters is uncertain; thus multiple settings are used and the best is chosen. This optimization can lead to inflated type I error. To correct this, permutation procedures can be used but they can be computationally intensive. Alternatively, a single parameter setting can be chosen a priori for the PRS, but choosing suboptimal settings result in loss of power. We propose computing PRSs under a range of parameter settings, performing principal component analysis (PCA) on the resulting set of PRSs, and using the first PRS-PC in association tests. The first PC reweights the variants included in the PRS with new weights to achieve maximum variation over all PRS settings used. Using simulations, we compare the performance of the proposed PRS-PCA approach with a permutation test and a priori selection of p-value threshold. We then apply the approach to the Mayo Clinic Bipolar Disorder Biobank study to test for PRS association with psychosis using a variety of PRSs constructed from summary statistics from the largest studies of psychiatric disorders and related traits. The PRS-PCA approach is simple to implement, outperforms the other strategies in most scenarios, and provides an unbiased estimate of prediction performance. We therefore recommend it to be used PRS association studies where multiple phenotypes and/or PRSs are being investigated.

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“…An important question to address is whether a PRS for schizophrenia developed from samples ascertained mainly in academic research settings would actually perform in real-world clinical settings. This is investigated in the article by Zheutlin et al (5) in this issue of the Journal.…”

mentioning

confidence: 97%

Polygenic Risk Scores in Schizophrenia: Ready for the Real World?

Binder

2019

AJP

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Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia in 106,160 Patients Across Four Health Care Systems

Cited by 184 publications

References 49 publications

A principal component approach to improve association testing with polygenic risk scores

A principal component approach to improve association testing with polygenic risk scores

A principal component approach to improve association testing with polygenic risk scores

Polygenic Risk Scores in Schizophrenia: Ready for the Real World?

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