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

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A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts. / Ni, Guiyan; Zeng, Jian; Revez, Joana A.; Wang, Ying; Zheng, Zhili; Ge, Tian; Restuadi, Restuadi; Kiewa, Jacqueline; Nyholt, Dale R.; Coleman, Jonathan R.I.; Smoller, Jordan W.; Ripke, Stephan; Neale, Benjamin M.; Corvin, Aiden; Walters, James T.R.; Farh, Kai How; Holmans, Peter A.; Lee, Phil; Bulik-Sullivan, Brendan; Collier, David A.; Huang, Hailiang; Pers, Tune H.; Agartz, Ingrid; Agerbo, Esben; Albus, Margot; Alexander, Madeline; Amin, Farooq; Bacanu, Silviu A.; Begemann, Martin; Belliveau, Richard A.; Bene, Judit; Bergen, Sarah E.; Bevilacqua, Elizabeth; Bigdeli, Tim B.; Black, Donald W.; Bruggeman, Richard; Buccola, Nancy G.; Hansen, Mark; Hansen, Thomas; Schizophrenia Working Group of the Psychiatric Genomics Consortium; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium.

In: Biological Psychiatry, Vol. 90, No. 9, 2021, p. 611-620.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Ni, G, Zeng, J, Revez, JA, Wang, Y, Zheng, Z, Ge, T, Restuadi, R, Kiewa, J, Nyholt, DR, Coleman, JRI, Smoller, JW, Ripke, S, Neale, BM, Corvin, A, Walters, JTR, Farh, KH, Holmans, PA, Lee, P, Bulik-Sullivan, B, Collier, DA, Huang, H, Pers, TH, Agartz, I, Agerbo, E, Albus, M, Alexander, M, Amin, F, Bacanu, SA, Begemann, M, Belliveau, RA, Bene, J, Bergen, SE, Bevilacqua, E, Bigdeli, TB, Black, DW, Bruggeman, R, Buccola, NG, Hansen, M, Hansen, T, Schizophrenia Working Group of the Psychiatric Genomics Consortium & Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium 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

APA

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., Ripke, S., Neale, B. M., Corvin, A., Walters, J. T. R., Farh, K. H., Holmans, P. A., Lee, P., Bulik-Sullivan, B., ... Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium (2021). A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts. Biological Psychiatry, 90(9), 611-620. https://doi.org/10.1016/j.biopsych.2021.04.018

Vancouver

Ni G, Zeng J, Revez JA, Wang Y, Zheng Z, Ge T et al. A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts. Biological Psychiatry. 2021;90(9):611-620. https://doi.org/10.1016/j.biopsych.2021.04.018

Author

Ni, Guiyan ; Zeng, Jian ; Revez, Joana A. ; Wang, Ying ; Zheng, Zhili ; Ge, Tian ; Restuadi, Restuadi ; Kiewa, Jacqueline ; Nyholt, Dale R. ; Coleman, Jonathan R.I. ; Smoller, Jordan W. ; Ripke, Stephan ; Neale, Benjamin M. ; Corvin, Aiden ; Walters, James T.R. ; Farh, Kai How ; Holmans, Peter A. ; Lee, Phil ; Bulik-Sullivan, Brendan ; Collier, David A. ; Huang, Hailiang ; Pers, Tune H. ; Agartz, Ingrid ; Agerbo, Esben ; Albus, Margot ; Alexander, Madeline ; Amin, Farooq ; Bacanu, Silviu A. ; Begemann, Martin ; Belliveau, Richard A. ; Bene, Judit ; Bergen, Sarah E. ; Bevilacqua, Elizabeth ; Bigdeli, Tim B. ; Black, Donald W. ; Bruggeman, Richard ; Buccola, Nancy G. ; Hansen, Mark ; Hansen, Thomas ; Schizophrenia Working Group of the Psychiatric Genomics Consortium ; Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. / A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts. In: Biological Psychiatry. 2021 ; Vol. 90, No. 9. pp. 611-620.

Bibtex

@article{b0bff97ceea74530a311d76e758e5e30,
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",
author = "Guiyan Ni and Jian Zeng and Revez, {Joana A.} and Ying Wang and Zhili Zheng and Tian Ge and Restuadi Restuadi and Jacqueline Kiewa and Nyholt, {Dale R.} and Coleman, {Jonathan R.I.} and Smoller, {Jordan W.} and Stephan Ripke and Neale, {Benjamin M.} and Aiden Corvin and Walters, {James T.R.} and Farh, {Kai How} and Holmans, {Peter A.} and Phil Lee and Brendan Bulik-Sullivan and Collier, {David A.} and Hailiang Huang and Pers, {Tune H.} and Ingrid Agartz and Esben Agerbo and Margot Albus and Madeline Alexander and Farooq Amin and Bacanu, {Silviu A.} and Martin Begemann and Belliveau, {Richard A.} and Judit Bene and Bergen, {Sarah E.} and Elizabeth Bevilacqua and Bigdeli, {Tim B.} and Black, {Donald W.} and Richard Bruggeman and Buccola, {Nancy G.} and Mark Hansen and Thomas Hansen and {Schizophrenia Working Group of the Psychiatric Genomics Consortium} and {Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium}",
note = "Publisher Copyright: {\textcopyright} 2021 Society of Biological Psychiatry",
year = "2021",
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",

}

RIS

TY - JOUR

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

AU - Ni, Guiyan

AU - Zeng, Jian

AU - Revez, Joana A.

AU - Wang, Ying

AU - Zheng, Zhili

AU - Ge, Tian

AU - Restuadi, Restuadi

AU - Kiewa, Jacqueline

AU - Nyholt, Dale R.

AU - Coleman, Jonathan R.I.

AU - Smoller, Jordan W.

AU - Ripke, Stephan

AU - Neale, Benjamin M.

AU - Corvin, Aiden

AU - Walters, James T.R.

AU - Farh, Kai How

AU - Holmans, Peter A.

AU - Lee, Phil

AU - Bulik-Sullivan, Brendan

AU - Collier, David A.

AU - Huang, Hailiang

AU - Pers, Tune H.

AU - Agartz, Ingrid

AU - Agerbo, Esben

AU - Albus, Margot

AU - Alexander, Madeline

AU - Amin, Farooq

AU - Bacanu, Silviu A.

AU - Begemann, Martin

AU - Belliveau, Richard A.

AU - Bene, Judit

AU - Bergen, Sarah E.

AU - Bevilacqua, Elizabeth

AU - Bigdeli, Tim B.

AU - Black, Donald W.

AU - Bruggeman, Richard

AU - Buccola, Nancy G.

AU - Hansen, Mark

AU - Hansen, Thomas

AU - Schizophrenia Working Group of the Psychiatric Genomics Consortium

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

N1 - Publisher Copyright: © 2021 Society of Biological Psychiatry

PY - 2021

Y1 - 2021

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

U2 - 10.1016/j.biopsych.2021.04.018

DO - 10.1016/j.biopsych.2021.04.018

M3 - Journal article

C2 - 34304866

AN - SCOPUS:85107783986

VL - 90

SP - 611

EP - 620

JO - Biological Psychiatry

JF - Biological Psychiatry

SN - 0006-3223

IS - 9

ER -

ID: 280176854