A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts

Schizophrenia Working Group of the Psychiatric Genomics Consortium; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium; Guiyan Ni; Jian Zeng; Joana A. Revez; Ying Wang; Zhili Zheng; Tian Ge; Restuadi Restuadi; Jacqueline Kiewa; Dale R. Nyholt; Jonathan R.I. Coleman; Jordan W. Smoller; Peter M. Visscher; Naomi R. Wray; Jian Yang

doi:10.1016/j.biopsych.2021.04.018

A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Guiyan Ni, Jian Zeng, Joana A. Revez, Ying Wang, Zhili Zheng, Tian Ge, Restuadi Restuadi, Jacqueline Kiewa, Dale R. Nyholt, Jonathan R.I. Coleman, Jordan W. Smoller, Peter M. Visscher, Naomi R. Wray^*, Jian Yang

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract / Description of output

Background: Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies. PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors. Methods: The Psychiatric Genomics Consortium Working Groups for schizophrenia and major depressive disorder bring together many independently collected case-control cohorts. We used these resources (31,328 schizophrenia cases, 41,191 controls; 248,750 major depressive disorder cases, 563,184 controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and 9 methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) were compared. Results: Compared with PC+T, the other 9 methods gave higher prediction statistics, MegaPRS, LDPred2, and SBayesR significantly so, explaining up to 9.2% variance in liability for schizophrenia across 30 target cohorts, an increase of 44%. For major depressive disorder across 26 target cohorts, these statistics were 3.5% and 59%, respectively. Conclusions: Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparisons and are recommended in applications to psychiatric disorders.

Original language	English
Pages (from-to)	611-620
Number of pages	10
Journal	Biological Psychiatry
Volume	90
Issue number	9
Early online date	3 May 2021
DOIs	https://doi.org/10.1016/j.biopsych.2021.04.018
Publication status	Published - 1 Nov 2021

Keywords / Materials (for Non-textual outputs)

Lassosum
LDpred2
Major depressive disorder
MegaPRS
Polygenic scores
PRS-CS
Psychiatric disorders
Risk prediction
SBayesR
Schizophrenia

Access to Document

10.1016/j.biopsych.2021.04.018

https://europepmc.org/article/MED/34304866

Cite this

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Ni, G., Zeng, J., Revez, J. A., Wang, Y., Zheng, Z., Ge, T., Restuadi, R., Kiewa, J., Nyholt, D. R., Coleman, J. R. I., Smoller, J. W., Visscher, P. M., Wray, N. R., & Yang, J. (2021). A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts. Biological Psychiatry, 90(9), 611-620. Advance online publication. https://doi.org/10.1016/j.biopsych.2021.04.018

@article{6bf9f0d439d044b2860f0d330471ff6c,

title = "A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts",

abstract = "Background: Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies. PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors. Methods: The Psychiatric Genomics Consortium Working Groups for schizophrenia and major depressive disorder bring together many independently collected case-control cohorts. We used these resources (31,328 schizophrenia cases, 41,191 controls; 248,750 major depressive disorder cases, 563,184 controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and 9 methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) were compared. Results: Compared with PC+T, the other 9 methods gave higher prediction statistics, MegaPRS, LDPred2, and SBayesR significantly so, explaining up to 9.2% variance in liability for schizophrenia across 30 target cohorts, an increase of 44%. For major depressive disorder across 26 target cohorts, these statistics were 3.5% and 59%, respectively. Conclusions: Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparisons and are recommended in applications to psychiatric disorders.",

keywords = "Lassosum, LDpred2, Major depressive disorder, MegaPRS, Polygenic scores, PRS-CS, Psychiatric disorders, Risk prediction, SBayesR, Schizophrenia",

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note = "Funding Information: The M{\"u}nster cohort was funded by the German Research Foundation (Grant No. FOR2107 DA1151/5-1 and DA1151/5-2 [to Udo Dannlowski] and Grant No. SFB-TRR58, Projects C09 and Z02 [to Udo Dannlowski]) and Interdisciplinary Center for Clinical Research of the Faculty of Medicine of M{\"u}nster (Grant No. Dan3/012/17 [to Udo Dannlowski]). Funding Information: This work was supported by the National Health and Medical Research Council (Grant Nos. 1173790 , 1078901 , and 108788 [to NRW] and Grant No. 1113400 [to NRW and PMV]) and the Australian Research Council (Grant No. FL180100072 [to PMV]). Funding Information: Statistical analyses were carried out on the Genetic Cluster Computer ( http://www.geneticcluster.org ) hosted by SURFsara and financially supported by the Netherlands Scientific Organization (Grant No. 480-05-003) along with a supplement from the Dutch Brain Foundation and the VU University Amsterdam. Funding Information: Some data used in this study were obtained from the database of Genotypes and Phenotypes (dbGaP). dbGaP Study Accession phs000021: Funding support for the Genome-Wide Association of Schizophrenia Study was provided by the National Institute of Mental Health (Grant Nos. R01 MH67257, R01 MH59588, R01 MH59571, R01 MH59565, R01 MH59587, R01 MH60870, R01 MH59566, R01 MH59586, R01 MH61675, R01 MH60879, R01 MH81800, U01 MH46276, U01 MH46289, U01 MH46318, U01 MH79469, and U01 MH79470), and the genotyping of samples was provided through the Genetic Association Information Network. Samples and associated phenotype data for the Genome-Wide Association of Schizophrenia Study were provided by the Molecular Genetics of Schizophrenia Collaboration (principal investigator P.V. Gejman, Evanston Northwestern Healthcare and Northwestern University, Evanston, IL). dbGaP accession phs000196: This work used in part data from the National Institute of Neurological Disorders and Stroke dbGaP database from the Center for Inherited Disease Research:NeuroGenetics Research Consortium Parkinson{\textquoteright}s Disease Study. dbGaP accession phs000187: High-Density SNP Association Analysis of Melanoma: Case-Control and Outcomes Investigation. Research support to collect data and develop an application to support this project was provided by the National Institutes of Health (Grant Nos. P50 CA093459, P50 CA097007, R01 ES011740, and R01 CA133996). Funding Information: This work would not have been possible without the contributions of the investigators who comprise the PGC SCZ and PGC MDD Working Groups. For a full list of acknowledgments of all individual cohorts included in PGC SCZ and PGCMDD Working Groups, please see the original publications. The PGC has received major funding from the National Institute of Mental Health (Grant No. U01 MH109528). Funding Information: This work was supported by the National Health and Medical Research Council (Grant Nos. 1173790, 1078901, and 108788 [to NRW] and Grant No. 1113400 [to NRW and PMV]) and the Australian Research Council (Grant No. FL180100072 [to PMV]). This work would not have been possible without the contributions of the investigators who comprise the PGC SCZ and PGC MDD Working Groups. For a full list of acknowledgments of all individual cohorts included in PGC SCZ and PGCMDD Working Groups, please see the original publications. The PGC has received major funding from the National Institute of Mental Health (Grant No. U01 MH109528). The M{\"u}nster cohort was funded by the German Research Foundation (Grant No. FOR2107 DA1151/5-1 and DA1151/5-2 [to Udo Dannlowski] and Grant No. SFB-TRR58, Projects C09 and Z02 [to Udo Dannlowski]) and Interdisciplinary Center for Clinical Research of the Faculty of Medicine of M{\"u}nster (Grant No. Dan3/012/17 [to Udo Dannlowski]). Some data used in this study were obtained from the database of Genotypes and Phenotypes (dbGaP). dbGaP Study Accession phs000021: Funding support for the Genome-Wide Association of Schizophrenia Study was provided by the National Institute of Mental Health (Grant Nos. R01 MH67257, R01 MH59588, R01 MH59571, R01 MH59565, R01 MH59587, R01 MH60870, R01 MH59566, R01 MH59586, R01 MH61675, R01 MH60879, R01 MH81800, U01 MH46276, U01 MH46289, U01 MH46318, U01 MH79469, and U01 MH79470), and the genotyping of samples was provided through the Genetic Association Information Network. Samples and associated phenotype data for the Genome-Wide Association of Schizophrenia Study were provided by the Molecular Genetics of Schizophrenia Collaboration (principal investigator P.V. Gejman, Evanston Northwestern Healthcare and Northwestern University, Evanston, IL). dbGaP accession phs000196: This work used in part data from the National Institute of Neurological Disorders and Stroke dbGaP database from the Center for Inherited Disease Research:NeuroGenetics Research Consortium Parkinson's Disease Study. dbGaP accession phs000187: High-Density SNP Association Analysis of Melanoma: Case-Control and Outcomes Investigation. Research support to collect data and develop an application to support this project was provided by the National Institutes of Health (Grant Nos. P50 CA093459, P50 CA097007, R01 ES011740, and R01 CA133996). Statistical analyses were carried out on the Genetic Cluster Computer (http://www.geneticcluster.org) hosted by SURFsara and financially supported by the Netherlands Scientific Organization (Grant No. 480-05-003) along with a supplement from the Dutch Brain Foundation and the VU University Amsterdam. We thank the customers, research participants, and employees of 23andMe for making this work possible. The study protocol used by 23andMe was approved by an external Association for the Accreditation of Human Research Protection Programs–accredited institutional review board. The authors report no biomedical financial interests or potential conflicts of interest. The PGC MDD Working Group is a collaborative coauthor of this article. The individual authors are Naomi R. Wray, Stephan Ripke, Manuel Mattheisen, Maciej Trzaskowski, Enda M. Byrne, Abdel Abdellaoui, Mark J. Adams, Esben Agerbo, Tracy M. Air, Till F.M. Andlauer, Silviu-Alin Bacanu, Marie B{\ae}kvad-Hansen, Aartjan T.F. Beekman, Tim B. Bigdeli, Elisabeth B. Binder, Julien Bryois, Henriette N. Buttensch{\o}n, Jonas Bybjerg-Grauholm, Na Cai, Enrique Castelao, Jane Hvarregaard Christensen, Toni-Kim Clarke, Jonathan R.I. Coleman, Luc{\'i}a Colodro-Conde, Baptiste Couvy-Duchesne, Nick Craddock, Gregory E. Crawford, Gail Davies, Ian J. Deary, Franziska Degenhardt, Eske M. Derks, Nese Direk, Conor V. Dolan, Erin C. Dunn, Thalia C. Eley, Valentina Escott-Price, Farnush Farhadi Hassan Kiadeh, Hilary K. Finucane, Jerome C. Foo, Andreas J. Forstner, Josef Frank, H{\'e}l{\'e}na A. Gaspar, Michael Gill, Fernando S. Goes, Scott D. Gordon, Jakob Grove, Lynsey S. Hall, Christine S{\o}holm Hansen, Thomas F. Hansen, Stefan Herms, Ian B. Hickie, Per Hoffmann, Georg Homuth, Carsten Horn, Jouke-Jan Hottenga, David M. Hougaard, David M. Howard, Marcus Ising, Rick Jansen, Ian Jones, Lisa A. Jones, Eric Jorgenson, James A. Knowles, Isaac S. Kohane, Julia Kraft, Warren W. Kretzschmar, Zolt{\'a}n Kutalik, Yihan Li, Penelope A. Lind, Donald J. MacIntyre, Dean F. MacKinnon, Robert M. Maier, Wolfgang Maier, Jonathan Marchini, Hamdi Mbarek, Patrick McGrath, Peter McGuffin, Sarah E. Medland, Divya Mehta, Christel M. Middeldorp, Evelin Mihailov, Yuri Milaneschi, Lili Milani, Francis M. Mondimore, Grant W. Montgomery, Sara Mostafavi, Niamh Mullins, Matthias Nauck, Bernard Ng, Michel G. Nivard, Dale R. Nyholt, Paul F. O'Reilly, Hogni Oskarsson, Michael J. Owen, Jodie N. Painter, Carsten B{\o}cker Pedersen, Marianne Gi{\o}rtz Pedersen, Roseann E. Peterson, Wouter J. Peyrot, Giorgio Pistis, Danielle Posthuma, Jorge A. Quiroz, Per Qvist, John P. Rice, Brien P. Riley, Margarita Rivera, Saira Saeed Mirza, Robert Schoevers, Eva C. Schulte, Ling Shen, Jianxin Shi, Stanley I. Shyn, Engilbert Sigurdsson, Grant C.B. Sinnamon, Johannes H. Smit, Daniel J. Smith, Hreinn Stefansson, Stacy Steinberg, Fabian Streit, Jana Strohmaier, Katherine E. Tansey, Henning Teismann, Alexander Teumer, Wesley Thompson, Pippa A. Thomson, Thorgeir E. Thorgeirsson, Matthew Traylor, Jens Treutlein, Vassily Trubetskoy, Andr{\'e} G. Uitterlinden, Daniel Umbricht, Sandra Van der Auwera, Albert M. van Hemert, Alexander Viktorin, Peter M. Visscher, Yunpeng Wang, Bradley T. Webb, Shantel Marie Weinsheimer, J{\"u}rgen Wellmann, Gonneke Willemsen, Stephanie H. Witt, Yang Wu, Hualin S. Xi, Jian Yang, Futao Zhang, Volker Arolt, Bernhard T. Baune, Klaus Berger, Dorret I. Boomsma, Sven Cichon, Udo Dannlowski, E.J.C. de Geus, J. Raymond DePaulo, Enrico Domenici, Katharina Domschke, T{\~o}nu Esko, Hans J. Grabe, Steven P. Hamilton, Caroline Hayward, Andrew C. Heath, Kenneth S. Kendler, Stefan Kloiber, Glyn Lewis, Qingqin S. Li, Susanne Lucae, Pamela A.F. Madden, Patrik K. Magnusson, Nicholas G. Martin, Andrew M. McIntosh, Andres Metspalu, Ole Mors, Preben Bo Mortensen, Bertram M{\"u}ller-Myhsok, Merete Nordentoft, Markus M. N{\"o}then, Michael C. O'Donovan, Sara A. Paciga, and Nancy L. Pedersen. (Affiliations are listed in Supplement 1.) Publisher Copyright: {\textcopyright} 2021 Society of Biological Psychiatry",

year = "2021",

month = nov,

day = "1",

doi = "10.1016/j.biopsych.2021.04.018",

language = "English",

volume = "90",

pages = "611--620",

journal = "Biological Psychiatry",

issn = "0006-3223",

publisher = "Elsevier",

number = "9",

}

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Ni, G, Zeng, J, Revez, JA, Wang, Y, Zheng, Z, Ge, T, Restuadi, R, Kiewa, J, Nyholt, DR, Coleman, JRI, Smoller, JW, Visscher, PM, Wray, NR & Yang, J 2021, 'A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts', Biological Psychiatry, vol. 90, no. 9, pp. 611-620. https://doi.org/10.1016/j.biopsych.2021.04.018

A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts. / Schizophrenia Working Group of the Psychiatric Genomics Consortium; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium; Ni, Guiyan et al.
In: Biological Psychiatry, Vol. 90, No. 9, 01.11.2021, p. 611-620.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts

AU - Schizophrenia Working Group of the Psychiatric Genomics Consortium

AU - Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium

AU - Ni, Guiyan

AU - Zeng, Jian

AU - Revez, Joana A.

AU - Wang, Ying

AU - Zheng, Zhili

AU - Ge, Tian

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AU - Umbricht, Daniel

AU - Van der Auwera, Sandra

AU - van Hemert, Albert M.

AU - Viktorin, Alexander

AU - Wang, Yunpeng

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AU - Willemsen, Gonneke

AU - Wu, Yang

AU - Xi, Hualin S.

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AU - Kloiber, Stefan

AU - Lewis, Glyn

AU - Lucae, Susanne

AU - Madden, Pamela A.F.

AU - Magnusson, Patrik K.

AU - Martin, Nicholas G.

AU - Nordentoft, Merete

AU - Paciga, Sara A.

AU - Pedersen, Nancy L.

N1 - Funding Information: The Münster cohort was funded by the German Research Foundation (Grant No. FOR2107 DA1151/5-1 and DA1151/5-2 [to Udo Dannlowski] and Grant No. SFB-TRR58, Projects C09 and Z02 [to Udo Dannlowski]) and Interdisciplinary Center for Clinical Research of the Faculty of Medicine of Münster (Grant No. Dan3/012/17 [to Udo Dannlowski]). Funding Information: This work was supported by the National Health and Medical Research Council (Grant Nos. 1173790 , 1078901 , and 108788 [to NRW] and Grant No. 1113400 [to NRW and PMV]) and the Australian Research Council (Grant No. FL180100072 [to PMV]). Funding Information: Statistical analyses were carried out on the Genetic Cluster Computer ( http://www.geneticcluster.org ) hosted by SURFsara and financially supported by the Netherlands Scientific Organization (Grant No. 480-05-003) along with a supplement from the Dutch Brain Foundation and the VU University Amsterdam. Funding Information: Some data used in this study were obtained from the database of Genotypes and Phenotypes (dbGaP). dbGaP Study Accession phs000021: Funding support for the Genome-Wide Association of Schizophrenia Study was provided by the National Institute of Mental Health (Grant Nos. R01 MH67257, R01 MH59588, R01 MH59571, R01 MH59565, R01 MH59587, R01 MH60870, R01 MH59566, R01 MH59586, R01 MH61675, R01 MH60879, R01 MH81800, U01 MH46276, U01 MH46289, U01 MH46318, U01 MH79469, and U01 MH79470), and the genotyping of samples was provided through the Genetic Association Information Network. Samples and associated phenotype data for the Genome-Wide Association of Schizophrenia Study were provided by the Molecular Genetics of Schizophrenia Collaboration (principal investigator P.V. Gejman, Evanston Northwestern Healthcare and Northwestern University, Evanston, IL). dbGaP accession phs000196: This work used in part data from the National Institute of Neurological Disorders and Stroke dbGaP database from the Center for Inherited Disease Research:NeuroGenetics Research Consortium Parkinson’s Disease Study. dbGaP accession phs000187: High-Density SNP Association Analysis of Melanoma: Case-Control and Outcomes Investigation. Research support to collect data and develop an application to support this project was provided by the National Institutes of Health (Grant Nos. P50 CA093459, P50 CA097007, R01 ES011740, and R01 CA133996). Funding Information: This work would not have been possible without the contributions of the investigators who comprise the PGC SCZ and PGC MDD Working Groups. For a full list of acknowledgments of all individual cohorts included in PGC SCZ and PGCMDD Working Groups, please see the original publications. The PGC has received major funding from the National Institute of Mental Health (Grant No. U01 MH109528). Funding Information: This work was supported by the National Health and Medical Research Council (Grant Nos. 1173790, 1078901, and 108788 [to NRW] and Grant No. 1113400 [to NRW and PMV]) and the Australian Research Council (Grant No. FL180100072 [to PMV]). This work would not have been possible without the contributions of the investigators who comprise the PGC SCZ and PGC MDD Working Groups. For a full list of acknowledgments of all individual cohorts included in PGC SCZ and PGCMDD Working Groups, please see the original publications. The PGC has received major funding from the National Institute of Mental Health (Grant No. U01 MH109528). The Münster cohort was funded by the German Research Foundation (Grant No. FOR2107 DA1151/5-1 and DA1151/5-2 [to Udo Dannlowski] and Grant No. SFB-TRR58, Projects C09 and Z02 [to Udo Dannlowski]) and Interdisciplinary Center for Clinical Research of the Faculty of Medicine of Münster (Grant No. Dan3/012/17 [to Udo Dannlowski]). Some data used in this study were obtained from the database of Genotypes and Phenotypes (dbGaP). dbGaP Study Accession phs000021: Funding support for the Genome-Wide Association of Schizophrenia Study was provided by the National Institute of Mental Health (Grant Nos. R01 MH67257, R01 MH59588, R01 MH59571, R01 MH59565, R01 MH59587, R01 MH60870, R01 MH59566, R01 MH59586, R01 MH61675, R01 MH60879, R01 MH81800, U01 MH46276, U01 MH46289, U01 MH46318, U01 MH79469, and U01 MH79470), and the genotyping of samples was provided through the Genetic Association Information Network. Samples and associated phenotype data for the Genome-Wide Association of Schizophrenia Study were provided by the Molecular Genetics of Schizophrenia Collaboration (principal investigator P.V. Gejman, Evanston Northwestern Healthcare and Northwestern University, Evanston, IL). dbGaP accession phs000196: This work used in part data from the National Institute of Neurological Disorders and Stroke dbGaP database from the Center for Inherited Disease Research:NeuroGenetics Research Consortium Parkinson's Disease Study. dbGaP accession phs000187: High-Density SNP Association Analysis of Melanoma: Case-Control and Outcomes Investigation. Research support to collect data and develop an application to support this project was provided by the National Institutes of Health (Grant Nos. P50 CA093459, P50 CA097007, R01 ES011740, and R01 CA133996). Statistical analyses were carried out on the Genetic Cluster Computer (http://www.geneticcluster.org) hosted by SURFsara and financially supported by the Netherlands Scientific Organization (Grant No. 480-05-003) along with a supplement from the Dutch Brain Foundation and the VU University Amsterdam. We thank the customers, research participants, and employees of 23andMe for making this work possible. The study protocol used by 23andMe was approved by an external Association for the Accreditation of Human Research Protection Programs–accredited institutional review board. The authors report no biomedical financial interests or potential conflicts of interest. The PGC MDD Working Group is a collaborative coauthor of this article. The individual authors are Naomi R. Wray, Stephan Ripke, Manuel Mattheisen, Maciej Trzaskowski, Enda M. Byrne, Abdel Abdellaoui, Mark J. Adams, Esben Agerbo, Tracy M. Air, Till F.M. Andlauer, Silviu-Alin Bacanu, Marie Bækvad-Hansen, Aartjan T.F. Beekman, Tim B. Bigdeli, Elisabeth B. Binder, Julien Bryois, Henriette N. Buttenschøn, Jonas Bybjerg-Grauholm, Na Cai, Enrique Castelao, Jane Hvarregaard Christensen, Toni-Kim Clarke, Jonathan R.I. Coleman, Lucía Colodro-Conde, Baptiste Couvy-Duchesne, Nick Craddock, Gregory E. Crawford, Gail Davies, Ian J. Deary, Franziska Degenhardt, Eske M. Derks, Nese Direk, Conor V. Dolan, Erin C. Dunn, Thalia C. Eley, Valentina Escott-Price, Farnush Farhadi Hassan Kiadeh, Hilary K. Finucane, Jerome C. Foo, Andreas J. Forstner, Josef Frank, Héléna A. Gaspar, Michael Gill, Fernando S. Goes, Scott D. Gordon, Jakob Grove, Lynsey S. Hall, Christine Søholm Hansen, Thomas F. Hansen, Stefan Herms, Ian B. Hickie, Per Hoffmann, Georg Homuth, Carsten Horn, Jouke-Jan Hottenga, David M. Hougaard, David M. Howard, Marcus Ising, Rick Jansen, Ian Jones, Lisa A. Jones, Eric Jorgenson, James A. Knowles, Isaac S. Kohane, Julia Kraft, Warren W. Kretzschmar, Zoltán Kutalik, Yihan Li, Penelope A. Lind, Donald J. MacIntyre, Dean F. MacKinnon, Robert M. Maier, Wolfgang Maier, Jonathan Marchini, Hamdi Mbarek, Patrick McGrath, Peter McGuffin, Sarah E. Medland, Divya Mehta, Christel M. Middeldorp, Evelin Mihailov, Yuri Milaneschi, Lili Milani, Francis M. Mondimore, Grant W. Montgomery, Sara Mostafavi, Niamh Mullins, Matthias Nauck, Bernard Ng, Michel G. Nivard, Dale R. Nyholt, Paul F. O'Reilly, Hogni Oskarsson, Michael J. Owen, Jodie N. Painter, Carsten Bøcker Pedersen, Marianne Giørtz Pedersen, Roseann E. Peterson, Wouter J. Peyrot, Giorgio Pistis, Danielle Posthuma, Jorge A. Quiroz, Per Qvist, John P. Rice, Brien P. Riley, Margarita Rivera, Saira Saeed Mirza, Robert Schoevers, Eva C. Schulte, Ling Shen, Jianxin Shi, Stanley I. Shyn, Engilbert Sigurdsson, Grant C.B. Sinnamon, Johannes H. Smit, Daniel J. Smith, Hreinn Stefansson, Stacy Steinberg, Fabian Streit, Jana Strohmaier, Katherine E. Tansey, Henning Teismann, Alexander Teumer, Wesley Thompson, Pippa A. Thomson, Thorgeir E. Thorgeirsson, Matthew Traylor, Jens Treutlein, Vassily Trubetskoy, André G. Uitterlinden, Daniel Umbricht, Sandra Van der Auwera, Albert M. van Hemert, Alexander Viktorin, Peter M. Visscher, Yunpeng Wang, Bradley T. Webb, Shantel Marie Weinsheimer, Jürgen Wellmann, Gonneke Willemsen, Stephanie H. Witt, Yang Wu, Hualin S. Xi, Jian Yang, Futao Zhang, Volker Arolt, Bernhard T. Baune, Klaus Berger, Dorret I. Boomsma, Sven Cichon, Udo Dannlowski, E.J.C. de Geus, J. Raymond DePaulo, Enrico Domenici, Katharina Domschke, Tõnu Esko, Hans J. Grabe, Steven P. Hamilton, Caroline Hayward, Andrew C. Heath, Kenneth S. Kendler, Stefan Kloiber, Glyn Lewis, Qingqin S. Li, Susanne Lucae, Pamela A.F. Madden, Patrik K. Magnusson, Nicholas G. Martin, Andrew M. McIntosh, Andres Metspalu, Ole Mors, Preben Bo Mortensen, Bertram Müller-Myhsok, Merete Nordentoft, Markus M. Nöthen, Michael C. O'Donovan, Sara A. Paciga, and Nancy L. Pedersen. (Affiliations are listed in Supplement 1.) Publisher Copyright: © 2021 Society of Biological Psychiatry

PY - 2021/11/1

Y1 - 2021/11/1

N2 - Background: Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies. PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors. Methods: The Psychiatric Genomics Consortium Working Groups for schizophrenia and major depressive disorder bring together many independently collected case-control cohorts. We used these resources (31,328 schizophrenia cases, 41,191 controls; 248,750 major depressive disorder cases, 563,184 controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and 9 methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) were compared. Results: Compared with PC+T, the other 9 methods gave higher prediction statistics, MegaPRS, LDPred2, and SBayesR significantly so, explaining up to 9.2% variance in liability for schizophrenia across 30 target cohorts, an increase of 44%. For major depressive disorder across 26 target cohorts, these statistics were 3.5% and 59%, respectively. Conclusions: Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparisons and are recommended in applications to psychiatric disorders.

AB - Background: Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies. PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors. Methods: The Psychiatric Genomics Consortium Working Groups for schizophrenia and major depressive disorder bring together many independently collected case-control cohorts. We used these resources (31,328 schizophrenia cases, 41,191 controls; 248,750 major depressive disorder cases, 563,184 controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and 9 methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) were compared. Results: Compared with PC+T, the other 9 methods gave higher prediction statistics, MegaPRS, LDPred2, and SBayesR significantly so, explaining up to 9.2% variance in liability for schizophrenia across 30 target cohorts, an increase of 44%. For major depressive disorder across 26 target cohorts, these statistics were 3.5% and 59%, respectively. Conclusions: Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparisons and are recommended in applications to psychiatric disorders.

KW - Lassosum

KW - LDpred2

KW - Major depressive disorder

KW - MegaPRS

KW - Polygenic scores

KW - PRS-CS

KW - Psychiatric disorders

KW - Risk prediction

KW - SBayesR

KW - Schizophrenia

UR - http://www.scopus.com/inward/record.url?scp=85107783986&partnerID=8YFLogxK

U2 - 10.1016/j.biopsych.2021.04.018

DO - 10.1016/j.biopsych.2021.04.018

M3 - Article

C2 - 34304866

AN - SCOPUS:85107783986

SN - 0006-3223

VL - 90

SP - 611

EP - 620

JO - Biological Psychiatry

JF - Biological Psychiatry

IS - 9

ER -

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Ni G, Zeng J, Revez JA, Wang Y et al. A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts. Biological Psychiatry. 2021 Nov 1;90(9):611-620. Epub 2021 May 3. doi: 10.1016/j.biopsych.2021.04.018

A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts

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